CDC Daily by State
davegoblue
2/20/2022
Background
This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.
Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.
The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.
The code in this module builds on code available in _v003, with function and mapping files updated:
- Generic_Added_Utility_Functions_202105_v001.R - generic functions that can be used in other areas
- Coronavirus_CDC_Daily_Functions_v001.R - functions specific to coronavirus daily data
Broadly, the CDC data analyzed by this module includes:
- CDC case and death data by state and date are available for download on the CDC website
- CDC hospital data are available for download on the healthdata.gov website
- CDC vaccines data are also available for download on the CDC website:
Functions and Mapping Files
The tidyverse package is loaded and functions are sourced:
# The tidyverse functions are routinely used without package::function format
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.1 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(geofacet)## Warning: package 'geofacet' was built under R version 4.1.2# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v001.R")A series of mapping files are also available to allow for parameterized processing. Mappings include:
- urlMapper - mapping file for urlType and url location to download data
- renMapper - mapping file for renaming of variables in the raw data file
- selfListMapper - mapping file for transformations by variable type
- fullListMapper - mapping file for transformations across variable types
- lstComboMapper - mapping file for elements to be combined by data type (most common is to combine NYC and NYS data to NY if the file provides them separately)
- uqMapper - mapping file for fields that should combine to be unique keys for processed files
- lstFilterMapper - mapping file for filtering to subset (most common is to keep 50 states and DC)
- vecSelectMapper - mapping file for variables to keep
- checkControlGroupMapper - mapping file for group_by() of control total checks
- checkControlAggMapper - mapping file for numeric variables for control total checks
- checkSimilarityMapper - mapping file for similarity checks to perform
- plotSimilarityMaooer - mapping file for fields where differences in universe should be plotted
- keyAggMapper - mapping file for the aggregate-level control total checks to perform
- perCapMapper - named vector that drives conversion from original field name to per capita field name
- hhsMapper - named vector that drivers numerical variables to keep (and renaming) from HHS capacity data file
These default parameters are maintained in a separate .R file and can be sourced:
source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")Example Code Processing
The function is run to download and process the latest CDC case, hospitalization, and death data:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220220.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220220.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220220.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220206")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220206")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220206")$dfRaw$vax
)
cdc_daily_220220 <- readRunCDCDaily(thruLabel="Feb 18, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 14
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-01-30 new_deaths 796 539 257 0.38501873
## 2 2022-01-29 new_deaths 1394 1098 296 0.23756019
## 3 2022-01-23 new_deaths 868 709 159 0.20164870
## 4 2022-01-22 new_deaths 1176 1028 148 0.13430127
## 5 2022-01-16 new_deaths 807 747 60 0.07722008
## 6 2022-01-25 new_deaths 3445 3220 225 0.06751688
## 7 2022-01-24 new_deaths 2679 2505 174 0.06712963
## 8 2022-01-27 new_deaths 2757 2592 165 0.06169377
## 9 2022-01-17 new_deaths 1429 1350 79 0.05685498
## 10 2022-01-26 new_deaths 3023 2858 165 0.05611291
## 11 2022-01-29 new_cases 195076 173891 21185 0.11483412
## 12 2022-01-30 new_cases 138089 124992 13097 0.09956629
## 13 2022-01-31 new_cases 620416 661083 40667 0.06346786
## 14 2022-02-04 new_cases 272825 289747 16922 0.06015941
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY tot_deaths 4003629 3992287 11342 0.002836948
## 2 AL tot_deaths 5972978 5963555 9423 0.001578850
## 3 NC tot_deaths 6808527 6798521 10006 0.001470708
## 4 FL tot_cases 1290393798 1286243847 4149951 0.003221214
## 5 MD tot_cases 232491171 231793719 697452 0.003004414
## 6 KY tot_cases 252489934 252077588 412346 0.001634453
## 7 FL new_deaths 68042 66007 2035 0.030362032
## 8 KY new_deaths 13402 13063 339 0.025618742
## 9 AL new_deaths 17741 17371 370 0.021075416
## 10 NC new_deaths 21278 21097 181 0.008542773
## 11 RI new_deaths 3358 3354 4 0.001191895
## 12 MD new_cases 984492 961805 22687 0.023312989
## 13 KY new_cases 1208554 1193647 14907 0.012411118
## 14 TN new_cases 1912511 1926401 13890 0.007236425
## 15 FL new_cases 5648704 5629602 19102 0.003387388
## 16 NC new_cases 2478266 2470242 8024 0.003242998
## 17 SC new_cases 1408611 1405271 3340 0.002373945
## 18 RI new_cases 348326 347901 425 0.001220866
## 19 PW new_cases 2498 2495 3 0.001201682
##
##
##
## Raw file for cdcDaily:
## Rows: 45,540
## Columns: 15
## $ date <date> 2021-12-01, 2020-08-17, 2021-05-31, 2021-07-20, 2020-0~
## $ state <chr> "ND", "MD", "CA", "MD", "VT", "IL", "VT", "MS", "NH", "~
## $ tot_cases <dbl> 163565, 100715, 3685032, 464491, 855, 1130917, 1009, 28~
## $ conf_cases <dbl> 135705, NA, 3685032, NA, NA, 1130917, NA, 176228, NA, 7~
## $ prob_cases <dbl> 27860, NA, 0, NA, NA, 0, NA, 103954, NA, 108997, 0, NA,~
## $ new_cases <dbl> 589, 503, 644, 155, 2, 2304, 10, 1059, 89, 1946, 180, 5~
## $ pnew_case <dbl> 220, 0, 0, 0, 0, 0, 0, 559, 0, 443, 0, 0, 0, 0, NA, 0, ~
## $ tot_deaths <dbl> 1907, 3765, 62011, 9822, 52, 21336, 54, 6730, 86, 12408~
## $ conf_death <dbl> NA, 3616, 62011, 9604, NA, 19306, NA, 4739, NA, 10976, ~
## $ prob_death <dbl> NA, 149, 0, 218, NA, 2030, NA, 1991, NA, 1432, NA, 416,~
## $ new_deaths <dbl> 9, 3, 5, 3, 0, 63, 0, 13, 2, 17, 0, 6, 0, -1, 0, 0, 8, ~
## $ pnew_death <dbl> 0, 0, 0, 1, 0, 16, 0, 7, 0, 2, 0, 0, 0, 0, NA, 0, 0, 4,~
## $ created_at <chr> "12/02/2021 02:35:20 PM", "08/19/2020 12:00:00 AM", "06~
## $ consent_cases <chr> "Agree", "N/A", "Agree", "N/A", "Not agree", "Agree", "~
## $ consent_deaths <chr> "Not agree", "Agree", "Agree", "Agree", "Not agree", "A~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-02-05 inp 108309 114478 6169 0.05538025
## 2 2022-02-05 hosp_ped 3323 3585 262 0.07585408
## 3 2021-11-24 hosp_ped 1387 1306 81 0.06015596
## 4 2022-02-05 hosp_adult 104794 110893 6099 0.05655417
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NH hosp_ped 725 811 86 0.111979167
## 2 ME hosp_ped 1373 1431 58 0.041369472
## 3 WV hosp_ped 4435 4554 119 0.026476805
## 4 VT hosp_ped 348 357 9 0.025531915
## 5 AR hosp_ped 10602 10393 209 0.019909502
## 6 KS hosp_ped 3856 3929 73 0.018754014
## 7 SC hosp_ped 7275 7393 118 0.016089446
## 8 VI hosp_ped 81 80 1 0.012422360
## 9 MA hosp_ped 9296 9412 116 0.012401112
## 10 ID hosp_ped 3155 3120 35 0.011155378
## 11 KY hosp_ped 15228 15375 147 0.009606901
## 12 NJ hosp_ped 15981 15838 143 0.008988340
## 13 IN hosp_ped 14697 14787 90 0.006105006
## 14 UT hosp_ped 7026 6998 28 0.003993155
## 15 NV hosp_ped 3856 3871 15 0.003882490
## 16 ND hosp_ped 2898 2909 11 0.003788531
## 17 TN hosp_ped 17497 17563 66 0.003764974
## 18 AL hosp_ped 17263 17319 56 0.003238679
## 19 NC hosp_ped 23574 23649 75 0.003176418
## 20 OR hosp_ped 7333 7356 23 0.003131595
## 21 MO hosp_ped 31461 31363 98 0.003119827
## 22 MS hosp_ped 8953 8926 27 0.003020303
## 23 PA hosp_ped 43632 43509 123 0.002823011
## 24 GA hosp_ped 42185 42079 106 0.002515902
## 25 IA hosp_ped 6153 6168 15 0.002434867
## 26 HI hosp_ped 1909 1913 4 0.002093145
## 27 AZ hosp_ped 22800 22847 47 0.002059281
## 28 NE hosp_ped 6181 6170 11 0.001781232
## 29 WA hosp_ped 10469 10484 15 0.001431776
## 30 CO hosp_ped 17474 17499 25 0.001429674
## 31 WI hosp_ped 8578 8568 10 0.001166453
## 32 IL hosp_ped 35034 35073 39 0.001112585
## 33 OK hosp_ped 20546 20524 22 0.001071342
## 34 RI hosp_ped 2843 2846 3 0.001054667
## 35 PR hosp_ped 16962 16979 17 0.001001738
## 36 AK hosp_ped 1996 1998 2 0.001001502
##
##
##
## Raw file for cdcHosp:
## Rows: 38,675
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 14
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 27,992
## Columns: 82
## $ date <date> 2022-02-19, 2022-02-19, 2022-0~
## $ MMWR_week <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7~
## $ state <chr> "NC", "TN", "MN", "MI", "SD", "~
## $ Distributed <dbl> 20744900, 12186030, 11914970, 1~
## $ Distributed_Janssen <dbl> 916100, 503900, 500200, 926300,~
## $ Distributed_Moderna <dbl> 7813760, 4644240, 4216760, 7835~
## $ Distributed_Pfizer <dbl> 12015040, 7037890, 7198010, 111~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 197795, 178441, 211272, 199329,~
## $ Distributed_Per_100k_12Plus <dbl> 230870, 208823, 249367, 231608,~
## $ Distributed_Per_100k_18Plus <dbl> 253377, 229098, 274762, 253818,~
## $ Distributed_Per_100k_65Plus <dbl> 1184680, 1065780, 1294570, 1127~
## $ vxa <dbl> 16040239, 9551129, 9853584, 150~
## $ Administered_12Plus <dbl> 15576577, 9369683, 9460637, 146~
## $ Administered_18Plus <dbl> 14630091, 8914389, 8826086, 138~
## $ Administered_65Plus <dbl> 4239236, 2778123, 2487485, 4293~
## $ Administered_Janssen <dbl> 508845, 259901, 353693, 459665,~
## $ Administered_Moderna <dbl> 5969173, 3654211, 3581985, 5900~
## $ Administered_Pfizer <dbl> 9561290, 5583600, 5913885, 8724~
## $ Administered_Unk_Manuf <dbl> 931, 53417, 4021, 2106, 133, 32~
## $ Admin_Per_100k <dbl> 152938, 139858, 174720, 151062,~
## $ Admin_Per_100k_12Plus <dbl> 173352, 160562, 198000, 170666,~
## $ Admin_Per_100k_18Plus <dbl> 178691, 167591, 203531, 176626,~
## $ Admin_Per_100k_65Plus <dbl> 242091, 242972, 270267, 243193,~
## $ Recip_Administered <dbl> 15939232, 9383280, 9868373, 153~
## $ Administered_Dose1_Recip <dbl> 8596653, 4180275, 4183752, 6576~
## $ Administered_Dose1_Pop_Pct <dbl> 82.0, 61.2, 74.2, 65.9, 74.7, 0~
## $ Administered_Dose1_Recip_12Plus <dbl> 8331132, 4079234, 3968078, 6351~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 92.7, 69.9, 83.0, 73.9, 86.3, 0~
## $ Administered_Dose1_Recip_18Plus <dbl> 7823417, 3850407, 3680383, 5966~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 72.4, 84.9, 76.1, 88.9, 0~
## $ Administered_Dose1_Recip_65Plus <dbl> 2154949, 1047531, 937204, 16884~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 91.6, 95.0, 95.0, 95.0, 0~
## $ vxc <dbl> 6201249, 3646584, 3830382, 5889~
## $ vxcpoppct <dbl> 59.1, 53.4, 67.9, 59.0, 59.7, 0~
## $ Series_Complete_12Plus <dbl> 6011177, 3568185, 3651613, 5701~
## $ Series_Complete_12PlusPop_Pct <dbl> 66.9, 61.1, 76.4, 66.3, 69.0, 0~
## $ vxcgte18 <dbl> 5622542, 3375377, 3382210, 5355~
## $ vxcgte18pct <dbl> 68.7, 63.5, 78.0, 68.3, 71.3, 0~
## $ vxcgte65 <dbl> 1498685, 956337, 876804, 153840~
## $ vxcgte65pct <dbl> 85.6, 83.6, 95.0, 87.1, 92.5, 0~
## $ Series_Complete_Janssen <dbl> 477185, 232189, 326034, 416641,~
## $ Series_Complete_Moderna <dbl> 2152155, 1299423, 1287594, 2139~
## $ Series_Complete_Pfizer <dbl> 3571765, 2103546, 2215307, 3333~
## $ Series_Complete_Unk_Manuf <dbl> 144, 11426, 1447, 1082, 34, 0, ~
## $ Series_Complete_Janssen_12Plus <dbl> 477158, 232135, 326016, 416612,~
## $ Series_Complete_Moderna_12Plus <dbl> 2152040, 1299371, 1287540, 2138~
## $ Series_Complete_Pfizer_12Plus <dbl> 3381836, 2025317, 2036626, 3144~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 143, 11362, 1431, 1073, 34, 0, ~
## $ Series_Complete_Janssen_18Plus <dbl> 475728, 231891, 325496, 416312,~
## $ Series_Complete_Moderna_18Plus <dbl> 2149019, 1298802, 1285260, 2138~
## $ Series_Complete_Pfizer_18Plus <dbl> 2997656, 1833427, 1770066, 2799~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 139, 11257, 1388, 986, 34, 0, 5~
## $ Series_Complete_Janssen_65Plus <dbl> 54321, 35691, 50477, 70861, 498~
## $ Series_Complete_Moderna_65Plus <dbl> 720300, 474871, 369087, 768663,~
## $ Series_Complete_Pfizer_65Plus <dbl> 723999, 439839, 456889, 698286,~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 65, 5936, 351, 592, 21, 0, 2511~
## $ Additional_Doses <dbl> 1544360, 1529958, 2125396, 2985~
## $ Additional_Doses_Vax_Pct <dbl> 24.9, 42.0, 55.5, 50.7, 39.8, 2~
## $ Additional_Doses_12Plus <dbl> 1544252, 1529687, 2125156, 2985~
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 25.7, 42.9, 58.2, 52.4, 41.2, 2~
## $ Additional_Doses_18Plus <dbl> 1500845, 1502838, 2049913, 2906~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 26.7, 44.5, 60.6, 54.3, 43.1, 2~
## $ Additional_Doses_50Plus <dbl> 1017165, 1059552, 1281534, 1976~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 33.8, 56.3, 72.6, 64.9, 54.8, 4~
## $ Additional_Doses_65Plus <dbl> 578981, 632802, 708477, 1135879~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 38.6, 66.2, 80.8, 73.8, 62.9, 5~
## $ Additional_Doses_Moderna <dbl> 680325, 649042, 857058, 1316220~
## $ Additional_Doses_Pfizer <dbl> 836934, 856740, 1239887, 162309~
## $ Additional_Doses_Janssen <dbl> 27082, 20983, 28141, 46218, 254~
## $ Additional_Doses_Unk_Manuf <dbl> 19, 3193, 310, 106, 9, 22, 648,~
## $ Administered_Dose1_Recip_5Plus <dbl> 8594663, 4179589, 4181728, 6576~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 87.0, 65.1, 79.1, 69.8, 80.3, 0~
## $ Series_Complete_5Plus <dbl> 6200658, 3646444, 3829701, 5889~
## $ Series_Complete_5PlusPop_Pct <dbl> 62.8, 56.8, 72.4, 62.5, 64.1, 0~
## $ Administered_5Plus <dbl> 16037693, 9550281, 9850893, 150~
## $ Admin_Per_100k_5Plus <dbl> 162353, 148745, 186287, 160139,~
## $ Distributed_Per_100k_5Plus <dbl> 210004, 189797, 225320, 211315,~
## $ Series_Complete_Moderna_5Plus <dbl> 2152112, 1299406, 1287586, 2138~
## $ Series_Complete_Pfizer_5Plus <dbl> 3571235, 2103464, 2214653, 3333~
## $ Series_Complete_Janssen_5Plus <dbl> 477168, 232150, 326019, 416627,~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 143, 11424, 1443, 1081, 34, 0, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.84e+10 3.17e+8 7.79e+7 910251 44781
## 2 after 1.83e+10 3.15e+8 7.73e+7 905741 38709
## 3 pctchg 4.83e- 3 4.32e-3 6.93e-3 0.00495 0.136
##
##
## Processed for cdcDaily:
## Rows: 38,709
## Columns: 6
## $ date <date> 2021-12-01, 2020-08-17, 2021-05-31, 2021-07-20, 2020-05-13~
## $ state <chr> "ND", "MD", "CA", "MD", "VT", "IL", "VT", "MS", "NH", "NC",~
## $ tot_cases <dbl> 163565, 100715, 3685032, 464491, 855, 1130917, 1009, 280182~
## $ tot_deaths <dbl> 1907, 3765, 62011, 9822, 52, 21336, 54, 6730, 86, 12408, 55~
## $ new_cases <dbl> 589, 503, 644, 155, 2, 2304, 10, 1059, 89, 1946, 180, 537, ~
## $ new_deaths <dbl> 9, 3, 5, 3, 0, 63, 0, 13, 2, 17, 0, 6, 0, -1, 0, 0, 8, 11, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.51e+7 3.88e+7 945228 38675
## 2 after 4.49e+7 3.86e+7 927959 37083
## 3 pctchg 4.84e-3 4.63e-3 0.0183 0.0412
##
##
## Processed for cdcHosp:
## Rows: 37,083
## Columns: 5
## $ date <date> 2020-10-14, 2020-10-14, 2020-10-11, 2020-10-10, 2020-10-09~
## $ state <chr> "HI", "NE", "IA", "NH", "HI", "DC", "KS", "NM", "ME", "NE",~
## $ inp <dbl> 111, 376, 497, 45, 110, 166, 474, 189, 23, 316, 546, 3246, ~
## $ hosp_adult <dbl> 111, 367, 487, 44, 108, 149, 454, 186, 23, 315, 534, 3104, ~
## $ hosp_ped <dbl> 0, 9, 10, 1, 2, 17, 5, 3, 0, 6, 12, 55, 8, 0, 1, 8, 2, 8, 6~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.66e+11 1.13e+11 1003870. 3.03e+10 1559557. 1.06e+11 1202494.
## 2 after 1.28e+11 5.46e+10 843159. 1.46e+10 1396120. 5.14e+10 1020516.
## 3 pctchg 5.20e- 1 5.16e- 1 0.160 5.16e- 1 0.105 5.17e- 1 0.151
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 22,083
## Columns: 9
## $ date <date> 2022-02-19, 2022-02-19, 2022-02-19, 2022-02-19, 2022-02-1~
## $ state <chr> "NC", "TN", "MN", "MI", "SD", "OH", "MT", "WV", "VA", "IA"~
## $ vxa <dbl> 16040239, 9551129, 9853584, 15086338, 1349798, 17152418, 1~
## $ vxc <dbl> 6201249, 3646584, 3830382, 5889772, 527824, 6712161, 59625~
## $ vxcpoppct <dbl> 59.1, 53.4, 67.9, 59.0, 59.7, 57.4, 55.8, 56.6, 71.7, 60.9~
## $ vxcgte65 <dbl> 1498685, 956337, 876804, 1538402, 140420, 1779459, 175563,~
## $ vxcgte65pct <dbl> 85.6, 83.6, 95.0, 87.1, 92.5, 87.0, 85.0, 83.5, 91.2, 92.0~
## $ vxcgte18 <dbl> 5622542, 3375377, 3382210, 5355218, 476217, 6118597, 54652~
## $ vxcgte18pct <dbl> 68.7, 63.5, 78.0, 68.3, 71.3, 67.2, 65.0, 65.8, 81.0, 71.7~
##
## Integrated per capita data file:
## Rows: 38,973
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220220, ovrWriteError=FALSE)The latest hospital data are downloaded:
# Run for latest data, save as RDS
indivHosp_20220221 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220221.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 399,863
## Columns: 109
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 7503
## 2 Critical Access Hospitals 106952
## 3 Long Term 27474
## 4 Short Term 257934
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 25
## 2 GU 160
## 3 MP 80
## 4 PR 4400
## 5 VI 160
##
## Record types for key metrics
## # A tibble: 8 x 5
## name `NA` Positive `Value -999999` Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 11667 387469 727 399863
## 2 all_adult_hospital_inpatient_bed_occupi~ 3328 364400 32135 399863
## 3 icu_beds_used_7_day_avg 1649 350757 47457 399863
## 4 inpatient_beds_7_day_avg 1730 396567 1566 399863
## 5 staffed_icu_adult_patients_confirmed_an~ 4251 279744 115868 399863
## 6 total_adult_patients_hospitalized_confi~ 2372 278715 118776 399863
## 7 total_beds_7_day_avg 6632 392858 373 399863
## 8 total_icu_beds_7_day_avg 2064 377884 19915 399863## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220221, ovrWriteError=FALSE)The post-processing capabilities are included:
# Create pivoted burden data
burdenPivotList_220220 <- postProcessCDCDaily(cdc_daily_220220,
dataThruLabel="Jan 2022",
keyDatesBurden=c("2022-01-31", "2021-07-31",
"2021-01-31", "2020-07-31"
),
keyDatesVaccine=c("2021-12-31", "2021-09-30",
"2021-06-30", "2021-03-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
The hospital summaries are also added:
# Can be run only as-needed
dfStateAgeBucket2019 <- readPopStateAge("./RInputFiles/sc-est2019-agesex-civ.csv") %>%
filterPopStateAge(keyCol="POPEST2019_CIV", keyColName="pop2019") %>%
bucketPopStateAge(popVar="pop2019")##
## -- Column specification --------------------------------------------------------
## cols(
## SUMLEV = col_character(),
## REGION = col_double(),
## DIVISION = col_double(),
## STATE = col_double(),
## NAME = col_character(),
## SEX = col_double(),
## AGE = col_double(),
## ESTBASE2010_CIV = col_double(),
## POPEST2010_CIV = col_double(),
## POPEST2011_CIV = col_double(),
## POPEST2012_CIV = col_double(),
## POPEST2013_CIV = col_double(),
## POPEST2014_CIV = col_double(),
## POPEST2015_CIV = col_double(),
## POPEST2016_CIV = col_double(),
## POPEST2017_CIV = col_double(),
## POPEST2018_CIV = col_double(),
## POPEST2019_CIV = col_double()
## )##
## *** File has been checked for uniqueness by: NAME SEX AGE
##
## [1] TRUE
## [1] TRUE
## [1] TRUE
##
## PASSED CHECK: United States total is the sum of states and DC
##
##
## PASSED CHECK: Age 999 total is the sum of the ages
##
##
## PASSED CHECK: Sex 0 total is the sum of the sexes
# Create hospitalized per capita data
hospPerCap_220220 <- hospAgePerCapita(dfStateAgeBucket2019,
lst=burdenPivotList_220220,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
The one-page CFR plot capability is included:
# Create CFR plots for select states
cfrStates <- list("FL"=list(keyState="FL", minDate="2020-08-01", multDeath=70),
"LA"=list(keyState="LA", minDate="2020-08-01", multDeath=80),
"CA"=list(keyState="CA", minDate="2020-08-01", multDeath=100),
"IL"=list(keyState="IL", minDate="2020-08-01", multDeath=100)
)
purrr::walk(cfrStates, .f=function(x) onePageCFRPlot(burdenPivotList_220220$dfPivot,
keyState=x$keyState,
minDate=x$minDate,
multDeath=x$multDeath
)
)
The peaks and valleys plots are included:
# Burden data
cdc_daily_220220$dfPerCapita %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
makePeakValley(numVar=c("new_deaths", "new_cases", "inp"),
windowWidth = 71,
rollMean=7,
facetVar=c("regn"),
fnNumVar=list("new_deaths"=function(x) x,
"new_cases"=function(x) x/1000,
"inp"=function(x) x/1000
),
fnPeak=list("new_deaths"=function(x) x+100,
"new_cases"=function(x) x+10,
"inp"=function(x) x+10
),
fnValley=list("new_deaths"=function(x) x-100,
"new_cases"=function(x) x-5,
"inp"=function(x) x-5
),
useTitle=c("new_deaths"="US coronavirus deaths",
"new_cases"="US coronavirus cases",
"inp"="US coronavirus total hospitalized"
),
yLab=c("new_deaths"="Rolling 7-day mean deaths",
"new_cases"="Rolling 7-day mean cases (000)",
"inp"="Rolling 7-day mean in hospital (000)"
)
)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## # A tibble: 3,107 x 11
## date regn new_deaths new_cases inp new_deaths_isPe~ new_cases_isPeak
## <date> <chr> <dbl> <dbl> <dbl> <lgl> <lgl>
## 1 2020-01-01 Nort~ NA NA NA FALSE FALSE
## 2 2020-01-01 South NA NA NA FALSE FALSE
## 3 2020-01-01 West NA NA NA FALSE FALSE
## 4 2020-01-02 Nort~ NA NA NA FALSE FALSE
## 5 2020-01-02 South NA NA NA FALSE FALSE
## 6 2020-01-02 West NA NA NA FALSE FALSE
## 7 2020-01-03 Nort~ NA NA NA FALSE FALSE
## 8 2020-01-03 South NA NA NA FALSE FALSE
## 9 2020-01-03 West NA NA NA FALSE FALSE
## 10 2020-01-04 Nort~ 0 0 0 FALSE FALSE
## # ... with 3,097 more rows, and 4 more variables: inp_isPeak <lgl>,
## # new_deaths_isValley <lgl>, new_cases_isValley <lgl>, inp_isValley <lgl># Vaccinations data for states with 8+ million population
cdc_daily_220220$dfPerCapita %>%
inner_join(getStateData(), by=c("state")) %>%
filter(pop >= 8000000) %>%
select(date, state, vxa, vxc) %>%
arrange(date, state) %>%
group_by(state) %>%
mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
ungroup() %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
filter(date >= "2020-12-01") %>%
makePeakValley(numVar=c("vxc", "vxa"),
windowWidth = 29,
rollMean=21,
facetVar=c("state"),
fnNumVar=list("vxa"=function(x) x/1000,
"vxc"=function(x) x/1000
),
fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
),
fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
),
fnGroupFacet=TRUE,
useTitle=c("vxa"="Vaccines adminsitered (US)",
"vxc"="Became fully vaccinated (US)"
),
yLab=c("vxa"="Rolling 21-day mean administered (000)",
"vxc"="Rolling 21-day mean completed (000)"
)
)## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 5,364 x 8
## date state vxc vxa vxc_isPeak vxa_isPeak vxc_isValley vxa_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # ... with 5,354 more rowsThe hospital utlization plots are included:
indivHosp_20220221 %>%
filter(state %in% c(state.abb, "DC"),
collection_week==max(collection_week)
) %>%
pull(hospital_pk) %>%
plotHospitalUtilization(df=indivHosp_20220221, keyHosp=., plotTitle="US Hospitals Summed")
Imputed hospital utilization data are also created, using functional form:
# Impute values for hospital capacity
imputeNACapacity <- function(df,
keyStates=c(state.abb, "DC"),
varMapper=hhsMapper,
varsToImpute=c("total_beds", "adult_beds"),
varUsedToImpute=c("inpatient_beds")
) {
# FUNCTION ARGUMENTS:
# df: the initial data frame
# keyState: states to include for filtering
# varMapper: variables to include and output names (named vector of form c("original name"="modified name"))
# varsToImpute: variables to be imputed
# varUsedToImpute: percent changes in this variable assumed to drive percent changes in varsToImpute if NA
df %>%
filter(state %in% all_of(keyStates)) %>%
colSelector(c("state", "collection_week", "hospital_pk", names(varMapper))) %>%
colRenamer(varMapper) %>%
mutate(across(where(is.numeric), .fns=function(x) ifelse(is.na(x), NA, ifelse(x==-999999, NA, x)))) %>%
arrange(hospital_pk, collection_week) %>%
group_by(hospital_pk) %>%
mutate(across(all_of(varsToImpute),
.fns=function(x) testImputeNA(x=x, y=get(varUsedToImpute), naValues=-999999)
)
) %>%
group_by(state, collection_week) %>%
summarize(across(where(is.numeric), .fns=sum, na.rm=TRUE), n=n(),.groups="drop")
}
modStateHosp_20220221 <- imputeNACapacity(indivHosp_20220221)The function is split so that it is more generic:
# Select and filter as needed
skinnyHHS <- function(df,
keyStates=c(state.abb, "DC"),
idCols=c("state", "collection_week", "hospital_pk"),
varMapper=hhsMapper
) {
# FUNCTION ARGUMENTS:
# df: the initial data frame
# keyState: states to include for filtering
# varMapper: variables to include and output names (named vector of form c("original name"="modified name"))
df %>%
filter(state %in% all_of(keyStates)) %>%
colSelector(c(all_of(idCols), names(varMapper))) %>%
colRenamer(varMapper)
}
# Impute values for hospital capacity
imputeNACapacity <- function(df,
extraNA=c(-999999),
convertAllNA=TRUE,
idVars=c("hospital_pk"),
sortVars=c("collection_week"),
varsToImpute=c("total_beds", "adult_beds"),
varUsedToImpute=c("inpatient_beds")
) {
# FUNCTION ARGUMENTS:
# df: the initial data frame
# extraNA: values that should be treated as NA
# convertAllNA: boolean, should all extraNA values be converted in all numeric columns?
# if FALSE, extraNA values will not be converted, though imputing will treat as NA
# varsToImpute: variables to be imputed
# varUsedToImpute: percent changes in this variable assumed to drive percent changes in varsToImpute if NA
# Convert NA if requested
if(isTRUE(convertAllNA)) {
df <- df %>%
mutate(across(where(is.numeric),
.fns=function(x) ifelse(is.na(x), NA, ifelse(x %in% all_of(extraNA), NA, x))
)
)
}
# Impute values and return data
df %>%
arrange(across(all_of(c(idVars, sortVars)))) %>%
group_by(across(all_of(idVars))) %>%
mutate(across(all_of(varsToImpute),
.fns=function(x) testImputeNA(x=x, y=get(varUsedToImpute), naValues=extraNA)
)
) %>%
ungroup()
}
sumImputedHHS <- function(df,
groupVars=c("state", "collection_week")) {
# FUNCTION ARGUMENTS:
# df: the initial data frame
# groupVars: variables for summing the data to
df %>%
group_by(across(all_of(groupVars))) %>%
summarize(across(where(is.numeric), .fns=sum, na.rm=TRUE), n=n(),.groups="drop")
}
identical(skinnyHHS(indivHosp_20220221) %>%
imputeNACapacity() %>%
sumImputedHHS(),
modStateHosp_20220221
)## [1] TRUEUpdated maps with imputed capacity are created:
modStateHosp_20220221 <- skinnyHHS(indivHosp_20220221) %>%
imputeNACapacity() %>%
sumImputedHHS()
# ICU summary
createGeoMap(modStateHosp_20220221,
yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"),
"pctICU"=c("label"="Total", "color"="black")
),
fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds),
"pctCovidICU"=expression(adult_icu_covid/icu_beds)
),
plotTitle="Average % ICU Capacity Filled by Week",
plotSubtitle="August 2020 to January 2022",
plotScaleLabel="% ICU\nUsed",
returnData=FALSE
)
# Adult beds summary
createGeoMap(modStateHosp_20220221 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"),
"pctAdult"=c("label"="Total", "color"="black")
),
fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds),
"pctCovidAdult"=expression(adult_beds_covid/adult_beds)
),
plotTitle="Average % Adult Beds Capacity Filled by Week",
plotSubtitle="August 2020 to January 2022\n(AK, CT, DE, and SD data excluded)",
plotScaleLabel="% Adult\nBeds\nUsed",
returnData=FALSE
)
Example Data Update
The function is run to download and process the latest data:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220304.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220304.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220304.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220220")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220220")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220220")$dfRaw$vax
)
cdc_daily_220304 <- readRunCDCDaily(thruLabel="Mar 2, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 12
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-03-03 tot_cases 175 188 13 0.07162534
## 2 2022-02-13 new_deaths 615 446 169 0.31856739
## 3 2022-02-06 new_deaths 609 472 137 0.25346901
## 4 2022-02-12 new_deaths 891 695 196 0.24716267
## 5 2022-02-05 new_deaths 1158 989 169 0.15742897
## 6 2022-01-30 new_deaths 869 796 73 0.08768769
## 7 2022-02-07 new_deaths 3177 3000 177 0.05730937
## 8 2022-02-08 new_deaths 3704 3504 200 0.05549390
## 9 2022-02-03 new_deaths 2653 2515 138 0.05340557
## 10 2022-01-29 new_deaths 1469 1394 75 0.05239260
## 11 2022-02-11 new_deaths 2775 2638 137 0.05061888
## 12 2020-03-03 new_cases 51 64 13 0.22608696
## 13 2022-02-12 new_cases 66377 55089 11288 0.18586271
## 14 2022-02-13 new_cases 47803 40950 6853 0.15442858
## 15 2022-01-30 new_cases 155259 138089 17170 0.11706233
## 16 2022-02-05 new_cases 102256 91295 10961 0.11326214
## 17 2022-02-14 new_cases 178028 199342 21314 0.11296075
## 18 2022-02-11 new_cases 155537 172496 16959 0.10339813
## 19 2022-01-29 new_cases 215839 195076 20763 0.10105740
## 20 2020-03-07 new_cases 146 160 14 0.09150327
## 21 2021-10-31 new_cases 22766 20850 1916 0.08785767
## 22 2021-11-06 new_cases 32140 29452 2688 0.08728406
## 23 2021-10-24 new_cases 25952 23899 2053 0.08236545
## 24 2021-11-07 new_cases 28368 26379 1989 0.07266152
## 25 2020-03-06 new_cases 130 121 9 0.07171315
## 26 2021-10-23 new_cases 33628 31349 2279 0.07014790
## 27 2022-01-22 new_cases 320403 299989 20414 0.06581000
## 28 2022-02-06 new_cases 96184 90271 5913 0.06342549
## 29 2022-01-18 new_cases 861976 917498 55522 0.06240271
## 30 2020-03-09 new_cases 390 415 25 0.06211180
## 31 2022-01-31 new_cases 583405 620416 37011 0.06148921
## 32 2022-01-23 new_cases 310096 291779 18317 0.06086646
## 33 2021-11-14 new_cases 30649 28992 1657 0.05556580
## 34 2021-11-20 new_cases 42759 40531 2228 0.05349982
## 35 2021-12-25 new_cases 126095 119545 6550 0.05333008
## 36 2021-10-30 new_cases 31410 29822 1588 0.05186830
## 37 2021-05-24 new_cases 15400 16206 806 0.05100297
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 RI tot_deaths 1395734 1419362 23628 0.016786639
## 2 FL tot_deaths 22083833 22194156 110323 0.004983198
## 3 KY tot_deaths 4208985 4192009 16976 0.004041427
## 4 NC tot_deaths 7123584 7111646 11938 0.001677247
## 5 AL tot_deaths 6231481 6222060 9421 0.001512983
## 6 RI tot_cases 75530417 79533518 4003101 0.051631619
## 7 ME tot_cases 38751314 36951188 1800126 0.047557900
## 8 WA tot_cases 263650336 264272454 622118 0.002356852
## 9 KY tot_cases 270113183 269767213 345970 0.001281654
## 10 AL new_deaths 18381 17877 504 0.027800761
## 11 FL new_deaths 70406 68581 1825 0.026261449
## 12 WA new_deaths 11615 11316 299 0.026078235
## 13 KY new_deaths 13885 13565 320 0.023315118
## 14 NC new_deaths 22277 22148 129 0.005807541
## 15 ME new_cases 225203 212435 12768 0.058349595
## 16 RI new_cases 336543 354045 17502 0.050687240
## 17 WA new_cases 1396813 1410596 13783 0.009819018
## 18 KY new_cases 1265367 1258310 7057 0.005592633
## 19 SD new_cases 234285 234961 676 0.002881218
## 20 NC new_cases 2563976 2559793 4183 0.001632782
## 21 SC new_cases 1451483 1449247 2236 0.001541681
##
##
##
## Raw file for cdcDaily:
## Rows: 46,260
## Columns: 15
## $ date <date> 2021-03-11, 2021-02-12, 2021-03-01, 2020-02-04, 2020-0~
## $ state <chr> "KS", "UT", "CO", "AR", "AR", "CO", "PW", "UT", "MA", "~
## $ tot_cases <dbl> 297229, 359641, 438745, 0, 56199, 1222893, 0, 636992, 7~
## $ conf_cases <dbl> 241035, 359641, 411869, NA, NA, 1117524, NA, 636992, 65~
## $ prob_cases <dbl> 56194, 0, 26876, NA, NA, 105369, NA, 0, 45550, 321, NA,~
## $ new_cases <dbl> 0, 1060, 677, 0, 547, 6962, 0, 0, 451, 619, 69, 24010, ~
## $ pnew_case <dbl> 0, 0, 60, NA, 0, 1247, 0, 0, 46, 1, 10, 4196, 264, 3202~
## $ tot_deaths <dbl> 4851, 1785, 5952, 0, 674, 10953, 0, 3787, 17818, 805, 8~
## $ conf_death <dbl> NA, 1729, 5218, NA, NA, 9666, NA, 3635, 17458, 624, NA,~
## $ prob_death <dbl> NA, 56, 734, NA, NA, 1287, NA, 152, 360, 181, NA, NA, 1~
## $ new_deaths <dbl> 0, 11, 1, 0, 11, 20, 0, 0, 5, 3, 0, 345, 8, 190, 0, 3, ~
## $ pnew_death <dbl> 0, 2, 0, NA, 0, 4, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, NA,~
## $ created_at <chr> "03/12/2021 03:20:13 PM", "02/13/2021 02:50:08 PM", "03~
## $ consent_cases <chr> "Agree", "Agree", "Agree", "Not agree", "Not agree", "A~
## $ consent_deaths <chr> "N/A", "Agree", "Agree", "Not agree", "Not agree", "Agr~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 11
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-02-20 inp 58908 62620 3712 0.06108880
## 2 2022-02-20 hosp_adult 56750 60478 3728 0.06360255
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 ND inp 110569 109813 756 0.006860814
## 2 WV hosp_ped 4497 4703 206 0.044782609
## 3 ME hosp_ped 1594 1538 56 0.035759898
## 4 MA hosp_ped 10034 9724 310 0.031379694
## 5 IN hosp_ped 15429 15261 168 0.010948192
## 6 KY hosp_ped 15750 15913 163 0.010295929
## 7 VA hosp_ped 14705 14555 150 0.010252905
## 8 NJ hosp_ped 16251 16415 164 0.010041021
## 9 NV hosp_ped 4105 4067 38 0.009300049
## 10 SC hosp_ped 7732 7661 71 0.009224972
## 11 AL hosp_ped 17872 17976 104 0.005802276
## 12 VT hosp_ped 360 362 2 0.005540166
## 13 KS hosp_ped 4025 4005 20 0.004981320
## 14 NM hosp_ped 6428 6457 29 0.004501358
## 15 IA hosp_ped 6509 6481 28 0.004311008
## 16 NH hosp_ped 761 758 3 0.003949967
## 17 FL hosp_ped 82260 82509 249 0.003022413
## 18 TN hosp_ped 18633 18581 52 0.002794647
## 19 WY hosp_ped 784 786 2 0.002547771
## 20 CO hosp_ped 18126 18084 42 0.002319801
## 21 SD hosp_ped 3899 3891 8 0.002053915
## 22 GA hosp_ped 43658 43742 84 0.001922197
## 23 AR hosp_ped 10931 10911 20 0.001831334
## 24 UT hosp_ped 7634 7621 13 0.001704359
## 25 CT hosp_ped 5640 5649 9 0.001594472
## 26 HI hosp_ped 2016 2019 3 0.001486989
## 27 MS hosp_ped 9380 9368 12 0.001280137
## 28 AZ hosp_ped 23979 23949 30 0.001251878
## 29 IL hosp_ped 36164 36121 43 0.001189735
## 30 MN hosp_ped 13210 13224 14 0.001059242
## 31 ND hosp_adult 104829 102042 2787 0.026944328
##
##
##
## Raw file for cdcHosp:
## Rows: 39,269
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 12
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 28,760
## Columns: 82
## $ date <date> 2022-03-03, 2022-03-03, 2022-0~
## $ MMWR_week <dbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9~
## $ state <chr> "NE", "NC", "TX", "CA", "AL", "~
## $ Distributed <dbl> 3775510, 20928600, 58996495, 86~
## $ Distributed_Janssen <dbl> 149600, 917900, 2609300, 368570~
## $ Distributed_Moderna <dbl> 1331380, 7886660, 21192040, 307~
## $ Distributed_Pfizer <dbl> 2294530, 12124040, 35195155, 51~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 195177, 199546, 203465, 217827,~
## $ Distributed_Per_100k_12Plus <dbl> 233430, 232915, 244787, 255803,~
## $ Distributed_Per_100k_18Plus <dbl> 258892, 255621, 273182, 281107,~
## $ Distributed_Per_100k_65Plus <dbl> 1208330, 1195170, 1579880, 1474~
## $ vxa <dbl> 3086667, 16146189, 44500682, 71~
## $ Administered_12Plus <dbl> 2984836, 15667506, 42929166, 69~
## $ Administered_18Plus <dbl> 2784330, 14709026, 39448235, 63~
## $ Administered_65Plus <dbl> 818837, 4254447, 8966187, 14693~
## $ Administered_Janssen <dbl> 93421, 510563, 1535569, 2278802~
## $ Administered_Moderna <dbl> 1110596, 6003546, 16331212, 267~
## $ Administered_Pfizer <dbl> 1876552, 9631145, 26629492, 426~
## $ Administered_Unk_Manuf <dbl> 6098, 935, 4409, 15243, 477, 21~
## $ Admin_Per_100k <dbl> 159566, 153948, 153472, 181390,~
## $ Admin_Per_100k_12Plus <dbl> 184544, 174364, 178121, 205442,~
## $ Admin_Per_100k_18Plus <dbl> 190925, 179655, 182664, 208987,~
## $ Admin_Per_100k_65Plus <dbl> 262063, 242959, 240108, 251683,~
## $ Recip_Administered <dbl> 3099534, 16045766, 43251399, 71~
## $ Administered_Dose1_Recip <dbl> 1343086, 8641769, 20646737, 323~
## $ Administered_Dose1_Pop_Pct <dbl> 69.4, 82.4, 71.2, 81.8, 61.9, 6~
## $ Administered_Dose1_Recip_12Plus <dbl> 1287672, 8369683, 19753177, 309~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 79.6, 93.1, 82.0, 91.9, 71.1, 7~
## $ Administered_Dose1_Recip_18Plus <dbl> 1192672, 7856851, 17992902, 284~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 81.8, 95.0, 83.3, 92.9, 73.8, 7~
## $ Administered_Dose1_Recip_65Plus <dbl> 306831, 2160730, 3621726, 59741~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc <dbl> 1210303, 6231369, 17444705, 278~
## $ vxcpoppct <dbl> 62.6, 59.4, 60.2, 70.5, 50.3, 5~
## $ Series_Complete_12Plus <dbl> 1164264, 6032933, 16838920, 267~
## $ Series_Complete_12PlusPop_Pct <dbl> 72.0, 67.1, 69.9, 79.5, 58.0, 6~
## $ vxcgte18 <dbl> 1078877, 5641201, 15431387, 245~
## $ vxcgte18pct <dbl> 74.0, 68.9, 71.5, 80.3, 60.3, 6~
## $ vxcgte65 <dbl> 284820, 1501131, 3209108, 51872~
## $ vxcgte65pct <dbl> 91.2, 85.7, 85.9, 88.9, 81.2, 8~
## $ Series_Complete_Janssen <dbl> 87478, 478497, 1339798, 2070288~
## $ Series_Complete_Moderna <dbl> 412033, 2159277, 6030460, 95834~
## $ Series_Complete_Pfizer <dbl> 709218, 3593448, 10073548, 1620~
## $ Series_Complete_Unk_Manuf <dbl> 1574, 147, 899, 4865, 654, 375,~
## $ Series_Complete_Janssen_12Plus <dbl> 87453, 478469, 1339351, 2069676~
## $ Series_Complete_Moderna_12Plus <dbl> 411994, 2159160, 6029642, 95826~
## $ Series_Complete_Pfizer_12Plus <dbl> 663259, 3395158, 9469060, 15076~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 1558, 146, 867, 4805, 654, 373,~
## $ Series_Complete_Janssen_18Plus <dbl> 87386, 477036, 1337802, 2062395~
## $ Series_Complete_Moderna_18Plus <dbl> 411823, 2156126, 6025533, 95567~
## $ Series_Complete_Pfizer_18Plus <dbl> 578185, 3007897, 8067213, 12955~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 1483, 142, 839, 4500, 650, 366,~
## $ Series_Complete_Janssen_65Plus <dbl> 6942, 54470, 177453, 201180, 36~
## $ Series_Complete_Moderna_65Plus <dbl> 138470, 721440, 1524709, 262127~
## $ Series_Complete_Pfizer_65Plus <dbl> 138496, 725155, 1506622, 236331~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 912, 66, 324, 1458, 415, 213, 1~
## $ Additional_Doses <dbl> 585237, 1574890, 6257276, 13507~
## $ Additional_Doses_Vax_Pct <dbl> 48.4, 25.3, 35.9, 48.5, 34.4, 3~
## $ Additional_Doses_12Plus <dbl> 585134, 1574750, 6256853, 13506~
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 50.3, 26.1, 37.2, 50.5, 34.9, 3~
## $ Additional_Doses_18Plus <dbl> 565479, 1527656, 6053696, 12967~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 52.4, 27.1, 39.2, 52.8, 36.3, 4~
## $ Additional_Doses_50Plus <dbl> 364760, 1031758, 3720211, 72429~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 65.5, 34.3, 52.0, 63.8, 47.2, 5~
## $ Additional_Doses_65Plus <dbl> 211563, 585640, 1945440, 368152~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 74.3, 39.0, 60.6, 71.0, 56.9, 6~
## $ Additional_Doses_Moderna <dbl> 229241, 693169, 2749043, 586417~
## $ Additional_Doses_Pfizer <dbl> 349233, 854191, 3412442, 742991~
## $ Additional_Doses_Janssen <dbl> 6431, 27508, 95589, 213372, 152~
## $ Additional_Doses_Unk_Manuf <dbl> 332, 22, 202, 522, 81, 490, 73,~
## $ Administered_Dose1_Recip_5Plus <dbl> 1342804, 8639422, 20641353, 323~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 74.5, 87.5, 76.4, 87.0, 65.9, 7~
## $ Series_Complete_5Plus <dbl> 1210243, 6230533, 17443348, 278~
## $ Series_Complete_5PlusPop_Pct <dbl> 67.1, 63.1, 64.6, 75.0, 53.5, 5~
## $ Administered_5Plus <dbl> 3086314, 16143045, 44494008, 71~
## $ Admin_Per_100k_5Plus <dbl> 171126, 163419, 164762, 192973,~
## $ Distributed_Per_100k_5Plus <dbl> 209340, 211864, 218465, 231812,~
## $ Series_Complete_Moderna_5Plus <dbl> 412011, 2159234, 6029941, 95831~
## $ Series_Complete_Pfizer_5Plus <dbl> 709196, 3592673, 10072988, 1620~
## $ Series_Complete_Janssen_5Plus <dbl> 87463, 478480, 1339521, 2069915~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 1573, 146, 898, 4864, 654, 373,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.93e+10 3.28e+8 7.85e+7 931901 45489
## 2 after 1.92e+10 3.27e+8 7.80e+7 927317 39321
## 3 pctchg 4.93e- 3 4.33e-3 6.98e-3 0.00492 0.136
##
##
## Processed for cdcDaily:
## Rows: 39,321
## Columns: 6
## $ date <date> 2021-03-11, 2021-02-12, 2021-03-01, 2020-02-04, 2020-08-22~
## $ state <chr> "KS", "UT", "CO", "AR", "AR", "CO", "UT", "MA", "HI", "TX",~
## $ tot_cases <dbl> 297229, 359641, 438745, 0, 56199, 1222893, 636992, 704796, ~
## $ tot_deaths <dbl> 4851, 1785, 5952, 0, 674, 10953, 3787, 17818, 883, 33124, 7~
## $ new_cases <dbl> 0, 1060, 677, 0, 547, 6962, 0, 451, 69, 24010, 1028, 18811,~
## $ new_deaths <dbl> 0, 11, 1, 0, 11, 20, 0, 5, 0, 345, 8, 190, 3, 15, 7, 8, 0, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.57e+7 3.93e+7 965351 39269
## 2 after 4.54e+7 3.91e+7 947960 37644
## 3 pctchg 4.81e-3 4.60e-3 0.0180 0.0414
##
##
## Processed for cdcHosp:
## Rows: 37,644
## Columns: 5
## $ date <date> 2020-10-18, 2020-10-13, 2020-10-12, 2020-10-08, 2020-10-06~
## $ state <chr> "VT", "NH", "ID", "MT", "HI", "NH", "NC", "DC", "MA", "MT",~
## $ inp <dbl> 2, 34, 221, 262, 124, 48, 1283, 156, 354, 207, 116, 102, 39~
## $ hosp_adult <dbl> 2, 34, 219, 259, 124, 48, 1246, 141, 347, 206, 109, 101, 38~
## $ hosp_ped <dbl> 0, 0, 2, 3, 0, 0, 34, 15, 7, 1, 3, 1, 10, 0, 0, 1, 6, 6, 7,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.79e+11 1.18e+11 1050491. 3.15e+10 1622354. 1.11e+11 1255957.
## 2 after 1.34e+11 5.72e+10 882060. 1.52e+10 1450421 5.37e+10 1065505.
## 3 pctchg 5.20e- 1 5.16e- 1 0.160 5.16e- 1 0.106 5.17e- 1 0.152
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 22,695
## Columns: 9
## $ date <date> 2022-03-03, 2022-03-03, 2022-03-03, 2022-03-03, 2022-03-0~
## $ state <chr> "NE", "NC", "TX", "CA", "AL", "SC", "WV", "MN", "CO", "KS"~
## $ vxa <dbl> 3086667, 16146189, 44500682, 71671126, 6108052, 7287794, 2~
## $ vxc <dbl> 1210303, 6231369, 17444705, 27867605, 2466221, 2880832, 10~
## $ vxcpoppct <dbl> 62.6, 59.4, 60.2, 70.5, 50.3, 56.0, 56.8, 68.3, 69.3, 60.3~
## $ vxcgte65 <dbl> 284820, 1501131, 3209108, 5187220, 689667, 807207, 306687,~
## $ vxcgte65pct <dbl> 91.2, 85.7, 85.9, 88.9, 81.2, 86.1, 83.6, 95.0, 92.0, 89.5~
## $ vxcgte18 <dbl> 1078877, 5641201, 15431387, 24579521, 2300814, 2646739, 94~
## $ vxcgte18pct <dbl> 74.0, 68.9, 71.5, 80.3, 60.3, 65.6, 66.0, 78.2, 79.1, 71.1~
##
## Integrated per capita data file:
## Rows: 39,534
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220304, ovrWriteError=FALSE)
# Run for latest data, save as RDS
indivHosp_20220304 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220304.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 409,797
## Columns: 109
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 7690
## 2 Critical Access Hospitals 109641
## 3 Long Term 28161
## 4 Short Term 264305
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 27
## 2 GU 164
## 3 MP 82
## 4 PR 4506
## 5 VI 164
##
## Record types for key metrics
## # A tibble: 8 x 5
## name `NA` Positive `Value -999999` Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 15604 393445 748 409797
## 2 all_adult_hospital_inpatient_bed_occupi~ 3318 373556 32923 409797
## 3 icu_beds_used_7_day_avg 1649 359635 48513 409797
## 4 inpatient_beds_7_day_avg 1730 406462 1605 409797
## 5 staffed_icu_adult_patients_confirmed_an~ 4241 286438 119118 409797
## 6 total_adult_patients_hospitalized_confi~ 2362 285557 121878 409797
## 7 total_beds_7_day_avg 10392 399022 383 409797
## 8 total_icu_beds_7_day_avg 2064 387368 20365 409797## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220304, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220304 <- postProcessCDCDaily(cdc_daily_220304,
dataThruLabel="Feb 2022",
keyDatesBurden=c("2022-02-28", "2021-08-31",
"2021-02-28", "2020-08-31"
),
keyDatesVaccine=c("2022-02-28", "2021-10-31",
"2021-06-30", "2021-02-28"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220304 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220304,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
Peaks and valleys are converted to functional form:
peakValleyCDCDaily <- function(df,
burdenVars=c("new_deaths", "new_cases", "inp"),
burdenWidth=71,
burdenRollMean=7,
minPopVax=8000000,
vaxVars=c("vxa", "vxc"),
minDateVax="2020-12-01",
vaxWidth=71,
vaxRollMean=21
) {
# FUNCTION ARGUMENTS
# df: data frame (can also pass a list that contains data frame "dfPerCapita")
# burdenVars: variables to be used for burden peaks and valleys
# burdenWidth: window size to be used for burden data
# burdenRollMean: rolling mean to use for smoothing burden data
# minPopVax: minimum population for state vaccines to be plotted
# vaxVars: variables to be used for vaccines peaks and valleys
# minDateVax: earliest day to use for vaccines plotting
# vaxWidth: window size to be used for vaccines data
# vaxRollMean: rolling mean to use for smoothing vaccines data
# Only works for specified burdenVars and vaxVars (fix)
if(!all.equal(sort(burdenVars), sort(c("new_deaths", "new_cases", "inp")))) stop("\nNot yet enabled - burden\n")
if(!all.equal(sort(vaxVars), sort(c("vxa", "vxc")))) stop("\nNot yet enabled - vaccines\n")
# Extract data frame from df if needed
if("list" %in% class(df)) df <- df[["dfPerCapita"]]
# Burden data
df %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
makePeakValley(numVar=burdenVars,
windowWidth = burdenWidth,
rollMean=burdenRollMean,
facetVar=c("regn"),
fnNumVar=list("new_deaths"=function(x) x,
"new_cases"=function(x) x/1000,
"inp"=function(x) x/1000
),
fnPeak=list("new_deaths"=function(x) x+100,
"new_cases"=function(x) x+10,
"inp"=function(x) x+10
),
fnValley=list("new_deaths"=function(x) x-100,
"new_cases"=function(x) x-5,
"inp"=function(x) x-5
),
useTitle=c("new_deaths"="US coronavirus deaths",
"new_cases"="US coronavirus cases",
"inp"="US coronavirus total hospitalized"
),
yLab=c("new_deaths"=paste0("Rolling ", burdenRollMean, "-day mean deaths"),
"new_cases"=paste0("Rolling ", burdenRollMean, "-day mean cases (000)"),
"inp"=paste0("Rolling ", burdenRollMean, "-day mean in hospital (000)")
)
)
# Vaccinations data for states with at least threshold population
df %>%
inner_join(getStateData(), by=c("state")) %>%
filter(pop >= minPopVax) %>%
select(c("state", "date", all_of(vaxVars))) %>%
arrange(date, state) %>%
group_by(state) %>%
mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
ungroup() %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
filter(date >= minDateVax) %>%
makePeakValley(numVar=vaxVars,
windowWidth = vaxWidth,
rollMean=vaxRollMean,
facetVar=c("state"),
fnNumVar=list("vxa"=function(x) x/1000,
"vxc"=function(x) x/1000
),
fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
),
fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
),
fnGroupFacet=TRUE,
useTitle=c("vxa"=paste0("Vaccines adminsitered (states with population >= ", minPopVax, ")"),
"vxc"=paste0("Became fully vaccinated (states with population >= ", minPopVax, ")")
),
yLab=c("vxa"=paste0("Rolling ", vaxRollMean, "-day mean administered (000)"),
"vxc"=paste0("Rolling ", vaxRollMean,"-day mean completed (000)")
)
)
}
peakValleyCDCDaily(cdc_daily_220304)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 5,496 x 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # ... with 5,486 more rowsHospital capacity maps with imputed capacity are created:
modStateHosp_20220304 <- skinnyHHS(indivHosp_20220304) %>%
imputeNACapacity() %>%
sumImputedHHS()
# ICU summary
createGeoMap(modStateHosp_20220304,
yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"),
"pctICU"=c("label"="Total", "color"="black")
),
fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds),
"pctCovidICU"=expression(adult_icu_covid/icu_beds)
),
plotTitle="Average % ICU Capacity Filled by Week",
plotSubtitle="August 2020 to February 2022",
plotScaleLabel="% ICU\nUsed",
returnData=FALSE
)
# Adult beds summary
createGeoMap(modStateHosp_20220304 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"),
"pctAdult"=c("label"="Total", "color"="black")
),
fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds),
"pctCovidAdult"=expression(adult_beds_covid/adult_beds)
),
plotTitle="Average % Adult Beds Capacity Filled by Week",
plotSubtitle="August 2020 to February 2022\n(AK, CT, DE, and SD data excluded)",
plotScaleLabel="% Adult\nBeds\nUsed",
returnData=FALSE
)
The latest data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220416.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220416.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220416.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220304")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220304")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220304")$dfRaw$vax
)
cdc_daily_220416 <- readRunCDCDaily(thruLabel="Apr 14, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-02-27 new_deaths 336 207 129 0.47513812
## 2 2022-02-26 new_deaths 553 416 137 0.28276574
## 3 2022-02-20 new_deaths 563 441 122 0.24302789
## 4 2021-07-30 new_deaths 651 521 130 0.22184300
## 5 2022-02-19 new_deaths 732 612 120 0.17857143
## 6 2022-02-13 new_deaths 693 615 78 0.11926606
## 7 2022-02-21 new_deaths 1089 967 122 0.11867704
## 8 2022-02-12 new_deaths 988 891 97 0.10324641
## 9 2022-02-06 new_deaths 674 609 65 0.10132502
## 10 2022-02-18 new_deaths 2283 2149 134 0.06046931
## 11 2022-02-05 new_deaths 1221 1158 63 0.05296343
## 12 2022-02-26 new_cases 26248 23158 3090 0.12508602
## 13 2021-10-31 new_cases 25456 22766 2690 0.11156733
## 14 2022-02-27 new_cases 18268 16411 1857 0.10709651
## 15 2022-02-28 new_cases 72092 80046 7954 0.10456296
## 16 2021-11-07 new_cases 31372 28368 3004 0.10056913
## 17 2021-11-06 new_cases 35485 32140 3345 0.09892791
## 18 2021-10-30 new_cases 34475 31410 3065 0.09304090
## 19 2021-11-14 new_cases 33631 30649 2982 0.09278158
## 20 2021-10-23 new_cases 36520 33628 2892 0.08245424
## 21 2021-10-24 new_cases 28146 25952 2194 0.08111206
## 22 2021-11-20 new_cases 45749 42759 2990 0.06756451
## 23 2021-11-21 new_cases 38274 35892 2382 0.06423429
## 24 2021-11-13 new_cases 53584 50305 3279 0.06312507
## 25 2021-10-25 new_cases 84093 88971 4878 0.05637221
## 26 2021-11-08 new_cases 116560 122589 6029 0.05042045
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY tot_deaths 4418134 4375794 42340 0.009629372
## 2 FL tot_deaths 23037457 22932268 105189 0.004576447
## 3 AL tot_deaths 6469661 6452309 17352 0.002685659
## 4 NC tot_deaths 7407674 7392943 14731 0.001990593
## 5 SC tot_deaths 5380045 5370547 9498 0.001766972
## 6 CO tot_cases 314009823 311159444 2850379 0.009118743
## 7 DE tot_cases 61116141 61473234 357093 0.005825839
## 8 KY tot_cases 286467313 285415770 1051543 0.003677475
## 9 NC tot_cases 592997222 592074229 922993 0.001557700
## 10 KY new_deaths 14951 13935 1016 0.070345496
## 11 DE new_deaths 2711 2573 138 0.052233157
## 12 AL new_deaths 19167 18407 760 0.040453505
## 13 FL new_deaths 72517 70789 1728 0.024116227
## 14 NC new_deaths 22958 22671 287 0.012579719
## 15 RI new_deaths 3441 3413 28 0.008170411
## 16 CO new_cases 1345585 1312298 33287 0.025047754
## 17 KY new_cases 1305049 1282281 22768 0.017599610
## 18 NC new_cases 2612332 2592991 19341 0.007431239
## 19 DE new_cases 257219 256051 1168 0.004551211
## 20 SC new_cases 1463332 1461843 1489 0.001018059
##
##
##
## Raw file for cdcDaily:
## Rows: 48,840
## Columns: 15
## $ date <date> 2022-01-14, 2022-01-02, 2020-08-22, 2020-07-17, 2020-0~
## $ state <chr> "KS", "AS", "AR", "MP", "AS", "HI", "MA", "PR", "GA", "~
## $ tot_cases <dbl> 621273, 11, 56199, 37, 0, 661, 704796, 35112, 1187107, ~
## $ conf_cases <dbl> 470516, NA, NA, 37, NA, NA, 659246, 34791, 937515, 3739~
## $ prob_cases <dbl> 150757, NA, NA, 0, NA, NA, 45550, 321, 249592, 101649, ~
## $ new_cases <dbl> 19414, 0, 547, 1, 0, 8, 451, 619, 3829, 1028, 0, 0, 276~
## $ pnew_case <dbl> 6964, 0, 0, 0, 0, 0, 46, 1, 1144, 264, 0, 0, 317, 0, 0,~
## $ tot_deaths <dbl> 7162, 0, 674, 2, 0, 17, 17818, 805, 21690, 7488, 0, 140~
## $ conf_death <dbl> NA, NA, NA, 2, NA, NA, 17458, 624, 18725, 6379, NA, 980~
## $ prob_death <dbl> NA, NA, NA, 0, NA, NA, 360, 181, 2965, 1109, NA, 4202, ~
## $ new_deaths <dbl> 21, 0, 11, 0, 0, 0, 5, 3, 7, 8, 0, 0, 3, 0, 69, 34, 0, ~
## $ pnew_death <dbl> 4, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ created_at <chr> "01/15/2022 02:59:30 PM", "01/03/2022 03:18:16 PM", "08~
## $ consent_cases <chr> "Agree", NA, "Not agree", "Agree", NA, "Not agree", "Ag~
## $ consent_deaths <chr> "N/A", NA, "Not agree", "Agree", NA, "Not agree", "Agre~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-03-03 inp 39007 41066 2059 0.05142807
## 2 2022-03-03 hosp_adult 37433 39443 2010 0.05229200
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NH hosp_ped 876 785 91 0.109572547
## 2 WV hosp_ped 4828 4653 175 0.036915937
## 3 MA hosp_ped 9943 10225 282 0.027965093
## 4 AR hosp_ped 10871 11128 257 0.023364698
## 5 NV hosp_ped 4183 4271 88 0.020818547
## 6 SC hosp_ped 7993 7878 115 0.014491840
## 7 KY hosp_ped 16368 16155 213 0.013098423
## 8 AL hosp_ped 18331 18119 212 0.011632373
## 9 VA hosp_ped 14832 14994 162 0.010863005
## 10 DE hosp_ped 4271 4236 35 0.008228518
## 11 ID hosp_ped 3406 3433 27 0.007895891
## 12 NJ hosp_ped 16512 16406 106 0.006440245
## 13 IN hosp_ped 15652 15740 88 0.005606524
## 14 UT hosp_ped 7997 8031 34 0.004242575
## 15 MD hosp_ped 12691 12739 48 0.003775069
## 16 PR hosp_ped 17374 17309 65 0.003748234
## 17 OK hosp_ped 21962 22043 81 0.003681400
## 18 TN hosp_ped 19088 19153 65 0.003399493
## 19 VT hosp_ped 382 383 1 0.002614379
## 20 FL hosp_ped 83322 83126 196 0.002355090
## 21 CO hosp_ped 18479 18439 40 0.002166965
## 22 PA hosp_ped 45833 45907 74 0.001613255
## 23 HI hosp_ped 2117 2120 3 0.001416096
## 24 CA hosp_ped 67230 67137 93 0.001384268
## 25 MO hosp_ped 33571 33616 45 0.001339545
## 26 WY hosp_ped 794 795 1 0.001258653
## 27 WA hosp_ped 11473 11486 13 0.001132454
## 28 LA hosp_ped 10021 10032 11 0.001097093
## 29 NH hosp_adult 90770 90907 137 0.001508171
##
##
##
## Raw file for cdcHosp:
## Rows: 41,591
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 43
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 31,512
## Columns: 82
## $ date <date> 2022-04-15, 2022-04-15, 2022-0~
## $ MMWR_week <dbl> 15, 15, 15, 15, 15, 15, 15, 15,~
## $ state <chr> "NV", "SC", "NE", "ND", "CA", "~
## $ Distributed <dbl> 5870110, 10352975, 3914910, 136~
## $ Distributed_Janssen <dbl> 258700, 451200, 150100, 52800, ~
## $ Distributed_Moderna <dbl> 2014400, 4306940, 1372780, 5258~
## $ Distributed_Pfizer <dbl> 3597010, 5594835, 2392030, 7883~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 190578, 201079, 202383, 179382,~
## $ Distributed_Per_100k_12Plus <dbl> 223896, 234232, 242048, 214517,~
## $ Distributed_Per_100k_18Plus <dbl> 245867, 256418, 268451, 234924,~
## $ Distributed_Per_100k_65Plus <dbl> 1183560, 1104880, 1252940, 1140~
## $ vxa <dbl> 4846899, 7387622, 3169859, 1070~
## $ Administered_12Plus <dbl> 4729850, 7215781, 3059482, 1035~
## $ Administered_18Plus <dbl> 4435348, 6828252, 2853809, 9829~
## $ Administered_65Plus <dbl> 1192563, 2256911, 847795, 29325~
## $ Administered_Janssen <dbl> 187265, 231115, 95046, 39681, 2~
## $ Administered_Moderna <dbl> 1659796, 2864880, 1138222, 4068~
## $ Administered_Pfizer <dbl> 2999310, 4289420, 1930052, 6234~
## $ Administered_Unk_Manuf <dbl> 528, 2207, 6539, 325, 15816, 47~
## $ Admin_Per_100k <dbl> 157359, 143485, 163867, 140451,~
## $ Admin_Per_100k_12Plus <dbl> 180405, 163254, 189160, 162456,~
## $ Admin_Per_100k_18Plus <dbl> 185772, 169119, 195690, 168926,~
## $ Admin_Per_100k_65Plus <dbl> 240450, 240860, 271331, 244697,~
## $ Recip_Administered <dbl> 4818924, 7391493, 3182205, 1049~
## $ Administered_Dose1_Recip <dbl> 2308282, 3462294, 1356028, 4934~
## $ Administered_Dose1_Pop_Pct <dbl> 74.9, 67.2, 70.1, 64.8, 83.2, 7~
## $ Administered_Dose1_Recip_12Plus <dbl> 2242948, 3363978, 1296952, 4757~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 85.5, 76.1, 80.2, 74.7, 93.2, 8~
## $ Administered_Dose1_Recip_18Plus <dbl> 2090215, 3165363, 1200872, 4491~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 87.5, 78.4, 82.3, 77.2, 94.2, 8~
## $ Administered_Dose1_Recip_65Plus <dbl> 502147, 950807, 308194, 120897,~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 95.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc <dbl> 1864022, 2914901, 1225968, 4170~
## $ vxcpoppct <dbl> 60.5, 56.6, 63.4, 54.7, 71.7, 6~
## $ Series_Complete_12Plus <dbl> 1813258, 2838179, 1175197, 4028~
## $ Series_Complete_12PlusPop_Pct <dbl> 69.2, 64.2, 72.7, 63.2, 80.5, 7~
## $ vxcgte18 <dbl> 1694297, 2670106, 1088392, 3803~
## $ vxcgte18pct <dbl> 71.0, 66.1, 74.6, 65.4, 81.3, 7~
## $ vxcgte65 <dbl> 415049, 810448, 286617, 102377,~
## $ vxcgte65pct <dbl> 83.7, 86.5, 91.7, 85.4, 89.9, 9~
## $ Series_Complete_Janssen <dbl> 172318, 207380, 88579, 36537, 2~
## $ Series_Complete_Moderna <dbl> 602890, 1039279, 415073, 145324~
## $ Series_Complete_Pfizer <dbl> 1088750, 1667852, 720640, 23513~
## $ Series_Complete_Unk_Manuf <dbl> 64, 390, 1676, 13, 5079, 1680, ~
## $ Series_Complete_Janssen_12Plus <dbl> 172314, 207331, 88553, 36530, 2~
## $ Series_Complete_Moderna_12Plus <dbl> 602884, 1039114, 415031, 145318~
## $ Series_Complete_Pfizer_12Plus <dbl> 1037996, 1591346, 669959, 22103~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 64, 388, 1654, 13, 5014, 1656, ~
## $ Series_Complete_Janssen_18Plus <dbl> 172270, 206747, 88485, 36382, 2~
## $ Series_Complete_Moderna_18Plus <dbl> 602778, 1037101, 414851, 145110~
## $ Series_Complete_Pfizer_18Plus <dbl> 919188, 1425877, 583491, 198891~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 61, 381, 1565, 12, 4699, 1605, ~
## $ Series_Complete_Janssen_65Plus <dbl> 26151, 31325, 6998, 4376, 20284~
## $ Series_Complete_Moderna_65Plus <dbl> 190706, 345825, 139225, 48336, ~
## $ Series_Complete_Pfizer_65Plus <dbl> 198156, 433078, 139467, 49660, ~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 36, 220, 927, 5, 1493, 392, 362~
## $ Additional_Doses <dbl> 713544, 1141995, 617137, 168496~
## $ Additional_Doses_Vax_Pct <dbl> 38.3, 39.2, 50.3, 40.4, 50.2, 5~
## $ Additional_Doses_12Plus <dbl> 713502, 1141860, 616974, 168479~
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 39.3, 40.2, 52.5, 41.8, 52.5, 6~
## $ Additional_Doses_18Plus <dbl> 694360, 1116070, 594777, 166705~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 41.0, 41.8, 54.6, 43.8, 54.7, 6~
## $ Additional_Doses_50Plus <dbl> 458721, 822790, 381496, 114424,~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 53.8, 53.4, 68.1, 57.9, 65.5, 7~
## $ Additional_Doses_65Plus <dbl> 259411, 507047, 220977, 69677, ~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 62.5, 62.6, 77.1, 68.1, 72.7, 8~
## $ Additional_Doses_Moderna <dbl> 292543, 489262, 242700, 77363, ~
## $ Additional_Doses_Pfizer <dbl> 408783, 629984, 367091, 88613, ~
## $ Additional_Doses_Janssen <dbl> 12214, 22236, 6918, 2511, 22302~
## $ Additional_Doses_Unk_Manuf <dbl> 4, 513, 428, 9, 575, 418, 61, 9~
## $ Administered_Dose1_Recip_5Plus <dbl> 2308180, 3460120, 1355714, 4931~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 79.7, 71.3, 75.2, 69.7, 88.5, 7~
## $ Series_Complete_5Plus <dbl> 1864007, 2913784, 1225903, 4168~
## $ Series_Complete_5PlusPop_Pct <dbl> 64.4, 60.0, 68.0, 58.9, 76.2, 7~
## $ Administered_5Plus <dbl> 4846790, 7384273, 3169464, 1069~
## $ Admin_Per_100k_5Plus <dbl> 167444, 152057, 175737, 151118,~
## $ Distributed_Per_100k_5Plus <dbl> 202797, 213189, 217070, 193090,~
## $ Series_Complete_Moderna_5Plus <dbl> 602885, 1039202, 415051, 145320~
## $ Series_Complete_Pfizer_5Plus <dbl> 1088743, 1666860, 720616, 23500~
## $ Series_Complete_Janssen_5Plus <dbl> 172315, 207334, 88563, 36532, 2~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 64, 388, 1673, 13, 5078, 1675, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.28e+10 3.70e+8 7.99e+7 968928 48026
## 2 after 2.26e+10 3.68e+8 7.93e+7 964235 41514
## 3 pctchg 5.25e- 3 4.38e-3 7.20e-3 0.00484 0.136
##
##
## Processed for cdcDaily:
## Rows: 41,514
## Columns: 6
## $ date <date> 2022-01-14, 2020-08-22, 2020-06-05, 2021-05-22, 2021-08-01~
## $ state <chr> "KS", "AR", "HI", "MA", "GA", "OK", "OK", "GA", "GA", "TX",~
## $ tot_cases <dbl> 621273, 56199, 661, 704796, 1187107, 475578, 1034439, 14780~
## $ tot_deaths <dbl> 7162, 674, 17, 17818, 21690, 7488, 14010, 3176, 1758, 49521~
## $ new_cases <dbl> 19414, 547, 8, 451, 3829, 1028, 0, 2766, 687, 1199, 0, 29, ~
## $ new_deaths <dbl> 21, 11, 0, 5, 7, 8, 0, 3, 69, 34, 0, 0, 31, 2, 15, 7, 0, 1,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.65e+7 4.01e+7 1012359 41591
## 2 after 4.63e+7 3.99e+7 994497 39837
## 3 pctchg 4.78e-3 4.57e-3 0.0176 0.0422
##
##
## Processed for cdcHosp:
## Rows: 39,837
## Columns: 5
## $ date <date> 2020-10-18, 2020-10-17, 2020-10-13, 2020-10-12, 2020-10-08~
## $ state <chr> "VT", "VT", "NH", "ID", "ND", "ID", "NE", "MS", "DC", "HI",~
## $ inp <dbl> 2, 3, 34, 221, 218, 191, 316, 516, 156, 123, 198, 116, 102,~
## $ hosp_adult <dbl> 2, 3, 34, 219, 212, 189, 315, 462, 141, 122, 193, 109, 101,~
## $ hosp_ped <dbl> 0, 0, 0, 2, 6, 2, 6, 4, 15, 1, 5, 3, 1, 1, 0, 0, 1, 6, 32, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.28e+11 1.37e+11 1219443. 3.57e+10 1848256. 1.28e+11 1448866.
## 2 after 1.58e+11 6.64e+10 1022671. 1.73e+10 1645640 6.19e+10 1227708.
## 3 pctchg 5.19e- 1 5.16e- 1 0.161 5.16e- 1 0.110 5.17e- 1 0.153
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 24,888
## Columns: 9
## $ date <date> 2022-04-15, 2022-04-15, 2022-04-15, 2022-04-15, 2022-04-1~
## $ state <chr> "NV", "SC", "NE", "ND", "CA", "MN", "DE", "WA", "AK", "CT"~
## $ vxa <dbl> 4846899, 7387622, 3169859, 1070327, 73947936, 10165098, 17~
## $ vxc <dbl> 1864022, 2914901, 1225968, 417012, 28314115, 3888695, 6696~
## $ vxcpoppct <dbl> 60.5, 56.6, 63.4, 54.7, 71.7, 69.0, 68.8, 72.3, 62.0, 78.8~
## $ vxcgte65 <dbl> 415049, 810448, 286617, 102377, 5246870, 882521, 180253, 1~
## $ vxcgte65pct <dbl> 83.7, 86.5, 91.7, 85.4, 89.9, 95.0, 95.0, 93.8, 85.9, 95.0~
## $ vxcgte18 <dbl> 1694297, 2670106, 1088392, 380395, 24878761, 3415346, 6047~
## $ vxcgte18pct <dbl> 71.0, 66.1, 74.6, 65.4, 81.3, 78.8, 78.5, 82.4, 73.0, 87.7~
##
## Integrated per capita data file:
## Rows: 41,727
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220416, ovrWriteError=FALSE)
# Run for latest data, save as RDS
indivHosp_20220416 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220416.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 439,873
## Columns: 109
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 8246
## 2 Critical Access Hospitals 117679
## 3 Long Term 30218
## 4 Short Term 283730
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 33
## 2 GU 176
## 3 MP 88
## 4 PR 4824
## 5 VI 176
##
## Record types for key metrics
## # A tibble: 8 x 5
## name `NA` Positive `Value -999999` Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 27909 411160 804 439873
## 2 all_adult_hospital_inpatient_bed_occupi~ 3318 400911 35644 439873
## 3 icu_beds_used_7_day_avg 1649 385635 52589 439873
## 4 inpatient_beds_7_day_avg 1730 436407 1736 439873
## 5 staffed_icu_adult_patients_confirmed_an~ 4241 306239 129393 439873
## 6 total_adult_patients_hospitalized_confi~ 2362 304424 133087 439873
## 7 total_beds_7_day_avg 22106 417354 413 439873
## 8 total_icu_beds_7_day_avg 2064 415848 21961 439873## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220416, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220416 <- postProcessCDCDaily(cdc_daily_220416,
dataThruLabel="Mar 2022",
keyDatesBurden=c("2022-03-31", "2021-09-30",
"2021-03-31", "2020-09-30"
),
keyDatesVaccine=c("2022-03-31", "2021-11-30",
"2021-07-31", "2021-03-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220416 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220416,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
Peaks and valleys of key metrics are also plotted:
peakValleyCDCDaily(cdc_daily_220416)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 6,012 x 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # ... with 6,002 more rowsHospital capacity maps with imputed capacity are created:
modStateHosp_20220416 <- skinnyHHS(indivHosp_20220416) %>%
imputeNACapacity() %>%
sumImputedHHS()
# ICU summary
createGeoMap(modStateHosp_20220416,
yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"),
"pctICU"=c("label"="Total", "color"="black")
),
fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds),
"pctCovidICU"=expression(adult_icu_covid/icu_beds)
),
plotTitle="Average % ICU Capacity Filled by Week",
plotSubtitle="August 2020 to mid-April 2022",
plotScaleLabel="% ICU\nUsed",
returnData=FALSE
)
# Adult beds summary
# createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"),
"pctAdult"=c("label"="Total", "color"="black")
),
fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds),
"pctCovidAdult"=expression(adult_beds_covid/adult_beds)
),
plotTitle="Average % Adult Beds Capacity Filled by Week",
plotSubtitle="August 2020 to mid-April 2022\n(AK, CT, DE, and SD data excluded)",
plotScaleLabel="% Adult\nBeds\nUsed",
returnData=FALSE
)
The latest data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220501.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220501.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220501.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220416")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_220416")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_220416")$dfRaw$vax
)
cdc_daily_220501 <- readRunCDCDaily(thruLabel="Apr 30, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2022-04-14 new_deaths 555 747 192 0.29493088
## 2 2022-04-10 new_deaths 64 49 15 0.26548673
## 3 2022-04-12 new_deaths 430 396 34 0.08232446
## 4 2022-04-13 new_deaths 596 641 45 0.07275667
## 5 2022-04-03 new_deaths 101 94 7 0.07179487
## 6 2022-04-11 new_deaths 324 303 21 0.06698565
## 7 2022-04-09 new_deaths 150 141 9 0.06185567
## 8 2022-04-02 new_deaths 162 154 8 0.05063291
## 9 2022-04-09 new_cases 14426 13247 1179 0.08520941
## 10 2022-04-13 new_cases 47095 51168 4073 0.08289997
## 11 2022-04-10 new_cases 18655 17702 953 0.05242457
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY tot_deaths 5084815 5070287 14528 0.002861222
## 2 CO tot_cases 373235234 372250769 984465 0.002641136
## 3 KY new_deaths 15445 15251 194 0.012640083
## 4 NV new_deaths 10223 10340 117 0.011379663
## 5 AL new_deaths 19552 19502 50 0.002560557
## 6 CO new_deaths 12001 12031 30 0.002496671
## 7 FL new_deaths 73846 73689 157 0.002128309
## 8 SC new_deaths 17733 17698 35 0.001975671
## 9 NC new_deaths 23362 23334 28 0.001199246
## 10 CO new_cases 1373102 1361600 11502 0.008411885
## 11 NC new_cases 2643272 2639241 4031 0.001526168
## 12 KY new_cases 1323254 1321450 1804 0.001364236
##
##
##
## Raw file for cdcDaily:
## Rows: 49,740
## Columns: 15
## $ date <date> 2022-01-14, 2022-01-02, 2020-08-22, 2020-07-17, 2020-0~
## $ state <chr> "KS", "AS", "AR", "MP", "AS", "CO", "MA", "PR", "GA", "~
## $ tot_cases <dbl> 621273, 11, 56199, 37, 0, 944337, 704796, 35112, 118710~
## $ conf_cases <dbl> 470516, NA, NA, 37, NA, 862950, 659246, 34791, 937515, ~
## $ prob_cases <dbl> 150757, NA, NA, 0, NA, 81387, 45550, 321, 249592, 94752~
## $ new_cases <dbl> 19414, 0, 547, 1, 0, 10817, 451, 619, 3829, 203, 0, 175~
## $ pnew_case <dbl> 6964, 0, 0, 0, 0, 931, 46, 1, 1144, 54, 0, 168, 317, 0,~
## $ tot_deaths <dbl> 7162, 0, 674, 2, 0, 10271, 17818, 805, 21690, 7256, 0, ~
## $ conf_death <dbl> NA, NA, NA, 2, NA, 9089, 17458, 624, 18725, 6176, 0, 28~
## $ prob_death <dbl> NA, NA, NA, 0, NA, 1182, 360, 181, 2965, 1080, 0, 5188,~
## $ new_deaths <dbl> 21, 0, 11, 0, 0, 31, 5, 3, 7, 0, 0, 20, 3, 0, 69, 34, 0~
## $ pnew_death <dbl> 4, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, -7, 0, 0, 0, 0, NA, 0,~
## $ created_at <chr> "01/15/2022 02:59:30 PM", "01/03/2022 03:18:16 PM", "08~
## $ consent_cases <chr> "Agree", NA, "Not agree", "Agree", NA, "Agree", "Agree"~
## $ consent_deaths <chr> "N/A", NA, "Not agree", "Agree", NA, "Agree", "Agree", ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: all_pediatric_inpatient_bed_occupied all_pediatric_inpatient_bed_occupied_coverage all_pediatric_inpatient_beds all_pediatric_inpatient_beds_coverage previous_day_admission_pediatric_covid_confirmed_0_4 previous_day_admission_pediatric_covid_confirmed_0_4_coverage previous_day_admission_pediatric_covid_confirmed_12_17 previous_day_admission_pediatric_covid_confirmed_12_17_coverage previous_day_admission_pediatric_covid_confirmed_5_11 previous_day_admission_pediatric_covid_confirmed_5_11_coverage previous_day_admission_pediatric_covid_confirmed_unknown previous_day_admission_pediatric_covid_confirmed_unknown_coverage staffed_icu_pediatric_patients_confirmed_covid staffed_icu_pediatric_patients_confirmed_covid_coverage staffed_pediatric_icu_bed_occupancy staffed_pediatric_icu_bed_occupancy_coverage total_staffed_pediatric_icu_beds total_staffed_pediatric_icu_beds_coverage
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-07-25 hosp_ped 4621 4270 351 0.07895625
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AS inp 354 356 2 0.005633803
## 2 VI inp 4288 4296 8 0.001863933
## 3 NH hosp_ped 898 944 46 0.049945711
## 4 ME hosp_ped 1889 1945 56 0.029212311
## 5 WV hosp_ped 5138 5034 104 0.020448289
## 6 AR hosp_ped 11326 11212 114 0.010116248
## 7 ID hosp_ped 3585 3550 35 0.009810792
## 8 AL hosp_ped 18802 18972 170 0.009000900
## 9 IN hosp_ped 16332 16230 102 0.006264971
## 10 NJ hosp_ped 16929 17023 94 0.005537229
## 11 MO hosp_ped 35405 35251 154 0.004359149
## 12 PR hosp_ped 17701 17774 73 0.004115574
## 13 CO hosp_ped 19247 19322 75 0.003889134
## 14 TN hosp_ped 20056 20133 77 0.003831894
## 15 NM hosp_ped 6922 6896 26 0.003763207
## 16 VA hosp_ped 15517 15465 52 0.003356788
## 17 UT hosp_ped 8779 8750 29 0.003308803
## 18 AK hosp_ped 2227 2233 6 0.002690583
## 19 MD hosp_ped 13808 13771 37 0.002683201
## 20 MS hosp_ped 9988 10011 23 0.002300115
## 21 KY hosp_ped 17263 17298 35 0.002025404
## 22 GA hosp_ped 46480 46386 94 0.002024422
## 23 CA hosp_ped 70500 70639 139 0.001969689
## 24 FL hosp_ped 85064 84922 142 0.001670726
## 25 IA hosp_ped 6846 6835 11 0.001608070
## 26 CT hosp_ped 6956 6966 10 0.001436575
## 27 SC hosp_ped 8214 8204 10 0.001218175
## 28 TX hosp_ped 103805 103689 116 0.001118105
## 29 NV hosp_ped 4552 4547 5 0.001099022
## 30 AS hosp_adult 350 352 2 0.005698006
## 31 VI hosp_adult 4039 4047 8 0.001978729
## 32 NH hosp_adult 92783 92686 97 0.001045997
##
##
##
## Raw file for cdcHosp:
## Rows: 42,401
## Columns: 135
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~
## $ all_pediatric_inpatient_bed_occupied <dbl> ~
## $ all_pediatric_inpatient_bed_occupied_coverage <dbl> ~
## $ all_pediatric_inpatient_beds <dbl> ~
## $ all_pediatric_inpatient_beds_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11 <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid <dbl> ~
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy <dbl> ~
## $ staffed_pediatric_icu_bed_occupancy_coverage <dbl> ~
## $ total_staffed_pediatric_icu_beds <dbl> ~
## $ total_staffed_pediatric_icu_beds_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 15
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 32,472
## Columns: 82
## $ date <date> 2022-04-30, 2022-04-30, 2022-0~
## $ MMWR_week <dbl> 17, 17, 17, 17, 17, 17, 17, 17,~
## $ state <chr> "IN", "NM", "US", "ME", "KY", "~
## $ Distributed <dbl> 13462080, 4474445, 728344715, 3~
## $ Distributed_Janssen <dbl> 607200, 187600, 30749100, 15400~
## $ Distributed_Moderna <dbl> 4734100, 1748900, 270415980, 13~
## $ Distributed_Pfizer <dbl> 8120780, 2537945, 427179635, 19~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 199965, 213391, 219375, 253996,~
## $ Distributed_Per_100k_12Plus <dbl> 235968, 250328, 256893, 288138,~
## $ Distributed_Per_100k_18Plus <dbl> 260679, 276031, 282020, 311698,~
## $ Distributed_Per_100k_65Plus <dbl> 1239900, 1184950, 1329290, 1196~
## $ vxa <dbl> 9524604, 3935871, 575765730, 28~
## $ Administered_12Plus <dbl> 9276622, 3800236, 557160223, 27~
## $ Administered_18Plus <dbl> 8736719, 3533433, 520923954, 26~
## $ Administered_65Plus <dbl> 2742875, 1042375, 146082778, 87~
## $ Administered_Janssen <dbl> 305604, 119590, 18703265, 14369~
## $ Administered_Moderna <dbl> 3431369, 1598596, 216827616, 11~
## $ Administered_Pfizer <dbl> 5756128, 2208144, 339679887, 15~
## $ Administered_Unk_Manuf <dbl> 31503, 9541, 554962, 3615, 2531~
## $ Admin_Per_100k <dbl> 141478, 187706, 173419, 211476,~
## $ Admin_Per_100k_12Plus <dbl> 162604, 212609, 196515, 232502,~
## $ Admin_Per_100k_18Plus <dbl> 169177, 217980, 201705, 237494,~
## $ Admin_Per_100k_65Plus <dbl> 252627, 276048, 266613, 307600,~
## $ Recip_Administered <dbl> 9536900, 4083307, 575765730, 28~
## $ Administered_Dose1_Recip <dbl> 4135482, 1837743, 257641065, 12~
## $ Administered_Dose1_Pop_Pct <dbl> 61.4, 87.6, 77.6, 90.4, 66.1, 8~
## $ Administered_Dose1_Recip_12Plus <dbl> 3990122, 1755545, 247398872, 11~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 69.9, 95.0, 87.3, 95.0, 75.2, 9~
## $ Administered_Dose1_Recip_18Plus <dbl> 3731041, 1618184, 229910032, 11~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 72.2, 95.0, 89.0, 95.0, 77.6, 9~
## $ Administered_Dose1_Recip_65Plus <dbl> 1009378, 428113, 56761008, 3298~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 93.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc <dbl> 3690380, 1490068, 219675939, 10~
## $ vxcpoppct <dbl> 54.8, 71.1, 66.2, 79.5, 57.4, 6~
## $ Series_Complete_12Plus <dbl> 3588448, 1428092, 211424055, 10~
## $ Series_Complete_12PlusPop_Pct <dbl> 62.9, 79.9, 74.6, 86.7, 65.4, 7~
## $ vxcgte18 <dbl> 3366149, 1314314, 196498734, 96~
## $ vxcgte18pct <dbl> 65.2, 81.1, 76.1, 88.2, 67.5, 7~
## $ vxcgte65 <dbl> 943206, 356210, 49434951, 28625~
## $ vxcgte65pct <dbl> 86.9, 94.3, 90.2, 95.0, 86.3, 9~
## $ Series_Complete_Janssen <dbl> 281574, 109986, 16953131, 13183~
## $ Series_Complete_Moderna <dbl> 1254015, 563214, 76433248, 3852~
## $ Series_Complete_Pfizer <dbl> 2146277, 814554, 126132484, 551~
## $ Series_Complete_Unk_Manuf <dbl> 8514, 2314, 157076, 845, 1838, ~
## $ Series_Complete_Janssen_12Plus <dbl> 281541, 109967, 16948031, 13180~
## $ Series_Complete_Moderna_12Plus <dbl> 1253947, 563150, 76426770, 3852~
## $ Series_Complete_Pfizer_12Plus <dbl> 2044486, 752672, 117894600, 510~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 8474, 2303, 154654, 837, 1819, ~
## $ Series_Complete_Janssen_18Plus <dbl> 281258, 109824, 16921288, 13174~
## $ Series_Complete_Moderna_18Plus <dbl> 1253587, 562729, 76344491, 3851~
## $ Series_Complete_Pfizer_18Plus <dbl> 1822963, 639497, 103084542, 448~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 8341, 2264, 148413, 755, 1713, ~
## $ Series_Complete_Janssen_65Plus <dbl> 31120, 21383, 2360115, 24650, 3~
## $ Series_Complete_Moderna_65Plus <dbl> 461227, 167308, 23565506, 13124~
## $ Series_Complete_Pfizer_65Plus <dbl> 446929, 166389, 23443158, 13006~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 3930, 1130, 66172, 294, 846, 36~
## $ Additional_Doses <dbl> 1693251, 739576, 100600067, 597~
## $ Additional_Doses_Vax_Pct <dbl> 45.9, 49.6, 45.8, 55.9, 44.2, 4~
## $ Additional_Doses_12Plus <dbl> 1689014, 739365, 100571760, 597~
## $ Additional_Doses_12Plus_Vax_Pct <dbl> 47.1, 51.8, 47.6, 58.1, 45.5, 4~
## $ Additional_Doses_18Plus <dbl> 1636009, 706410, 96911167, 5754~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 48.6, 53.7, 49.3, 59.6, 47.2, 4~
## $ Additional_Doses_50Plus <dbl> 1114515, 442723, 61293070, 3915~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 59.7, 63.5, 60.7, 70.1, 59.1, 6~
## $ Additional_Doses_65Plus <dbl> 652191, 246351, 33899976, 22276~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 69.1, 69.2, 68.6, 77.8, 68.5, 6~
## $ Additional_Doses_Moderna <dbl> 659070, 310369, 43251133, 27256~
## $ Additional_Doses_Pfizer <dbl> 1011148, 418139, 55814930, 3131~
## $ Additional_Doses_Janssen <dbl> 21456, 10800, 1502478, 10855, 2~
## $ Additional_Doses_Unk_Manuf <dbl> 1577, 268, 31526, 622, 220, 59,~
## $ Administered_Dose1_Recip_5Plus <dbl> 4135027, 1837487, 257532058, 12~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 65.5, 93.0, 82.5, 94.9, 70.4, 8~
## $ Series_Complete_5Plus <dbl> 3690294, 1490006, 219626527, 10~
## $ Series_Complete_5PlusPop_Pct <dbl> 58.4, 75.4, 70.3, 83.5, 61.1, 7~
## $ Administered_5Plus <dbl> 9524148, 3935594, 575608906, 28~
## $ Admin_Per_100k_5Plus <dbl> 150845, 199186, 184333, 221953,~
## $ Distributed_Per_100k_5Plus <dbl> 213214, 226458, 233246, 266598,~
## $ Series_Complete_Moderna_5Plus <dbl> 1254006, 563197, 76430070, 3852~
## $ Series_Complete_Pfizer_5Plus <dbl> 2146218, 814520, 126089538, 551~
## $ Series_Complete_Janssen_5Plus <dbl> 281557, 109976, 16950044, 13181~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 8513, 2313, 156875, 845, 1837, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.40e+10 3.85e+8 8.07e+7 974312 48911
## 2 after 2.39e+10 3.83e+8 8.00e+7 969594 42279
## 3 pctchg 5.35e- 3 4.40e-3 7.66e-3 0.00484 0.136
##
##
## Processed for cdcDaily:
## Rows: 42,279
## Columns: 6
## $ date <date> 2022-01-14, 2020-08-22, 2021-12-31, 2021-05-22, 2021-08-01~
## $ state <chr> "KS", "AR", "CO", "MA", "GA", "OK", "GA", "GA", "TX", "AK",~
## $ tot_cases <dbl> 621273, 56199, 944337, 704796, 1187107, 449170, 147804, 383~
## $ tot_deaths <dbl> 7162, 674, 10271, 17818, 21690, 7256, 3176, 1758, 49521, 9,~
## $ new_cases <dbl> 19414, 547, 10817, 451, 3829, 203, 2766, 687, 1199, 7, 1723~
## $ new_deaths <dbl> 21, 11, 31, 5, 7, 0, 3, 69, 34, 0, 127, 31, 0, 9, 15, 7, 0,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 4.68e+7 4.03e+7 1027403 42401
## 2 after 4.66e+7 4.01e+7 1008991 40602
## 3 pctchg 4.82e-3 4.60e-3 0.0179 0.0424
##
##
## Processed for cdcHosp:
## Rows: 40,602
## Columns: 5
## $ date <date> 2020-10-13, 2020-10-12, 2020-10-09, 2020-10-04, 2020-09-22~
## $ state <chr> "RI", "VT", "RI", "RI", "VT", "RI", "AK", "RI", "DE", "VT",~
## $ inp <dbl> 124, 0, 116, 92, 1, 85, 50, 75, 89, 6, 12, 160, 69, 33, 70,~
## $ hosp_adult <dbl> 123, 0, 115, 91, 1, 84, 49, 75, 84, 5, 9, 115, 66, 33, 55, ~
## $ hosp_ped <dbl> 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 3, 46, 0, 0, 15, 147, 0, 1, 0~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.45e+11 1.44e+11 1278961. 3.72e+10 1927715. 1.34e+11 1516669.
## 2 after 1.66e+11 6.96e+10 1072129. 1.80e+10 1714305. 6.48e+10 1284690.
## 3 pctchg 5.19e- 1 5.16e- 1 0.162 5.16e- 1 0.111 5.16e- 1 0.153
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 25,653
## Columns: 9
## $ date <date> 2022-04-30, 2022-04-30, 2022-04-30, 2022-04-30, 2022-04-3~
## $ state <chr> "IN", "NM", "ME", "KY", "DE", "MA", "CA", "TX", "WA", "SC"~
## $ vxa <dbl> 9524604, 3935871, 2842688, 6522218, 1813671, 14845867, 750~
## $ vxc <dbl> 3690380, 1490068, 1069169, 2563070, 672952, 5441750, 28464~
## $ vxcpoppct <dbl> 54.8, 71.1, 79.5, 57.4, 69.1, 79.0, 72.0, 61.4, 72.6, 56.7~
## $ vxcgte65 <dbl> 943206, 356210, 286250, 647476, 181583, 1124456, 5280949, ~
## $ vxcgte65pct <dbl> 86.9, 94.3, 95.0, 86.3, 95.0, 95.0, 90.5, 87.3, 94.2, 86.6~
## $ vxcgte18 <dbl> 3366149, 1314314, 965876, 2338367, 607641, 4805717, 249983~
## $ vxcgte18pct <dbl> 65.2, 81.1, 88.2, 67.5, 78.9, 86.8, 81.6, 72.5, 82.6, 66.2~
##
## Integrated per capita data file:
## Rows: 42,492
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220501, ovrWriteError=FALSE)
# Run for latest data, save as RDS
indivHosp_20220501 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220501.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 449,805
## Columns: 109
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ inpatient_beds_used_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ inpatient_beds_used_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ inpatient_beds_used_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 8432
## 2 Critical Access Hospitals 120273
## 3 Long Term 30899
## 4 Short Term 290201
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 35
## 2 GU 180
## 3 MP 90
## 4 PR 4930
## 5 VI 180
##
## Record types for key metrics
## # A tibble: 8 x 5
## name `NA` Positive `Value -999999` Total
## <chr> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_avg 32098 416881 826 449805
## 2 all_adult_hospital_inpatient_bed_occupi~ 3318 409902 36585 449805
## 3 icu_beds_used_7_day_avg 1650 394165 53990 449805
## 4 inpatient_beds_7_day_avg 1728 446299 1778 449805
## 5 staffed_icu_adult_patients_confirmed_an~ 4238 313313 132254 449805
## 6 total_adult_patients_hospitalized_confi~ 2359 310541 136905 449805
## 7 total_beds_7_day_avg 26089 423289 427 449805
## 8 total_icu_beds_7_day_avg 2065 425219 22521 449805## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220501, ovrWriteError=FALSE)Post-processing is run, including hospital summaries:
# Create pivoted burden data
burdenPivotList_220501 <- postProcessCDCDaily(cdc_daily_220501,
dataThruLabel="Apr 2022",
keyDatesBurden=c("2022-04-29", "2021-10-31",
"2021-04-30", "2020-10-31"
),
keyDatesVaccine=c("2022-04-29", "2021-12-31",
"2021-08-31", "2021-04-30"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220501 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220501,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
The post-process function is working incorrectly on the pediatric data. It appears that a few category labels changed syntax. Function createBurdenPivot() is updated to a better formed grep sequence for the latest data:
burdenPivotList_220501$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n())## `summarise()` has grouped output by 'adultPed', 'confSusp', 'age'. You can override using the `.groups` argument.## # A tibble: 19 x 6
## # Groups: adultPed, confSusp, age [18]
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_co~ 4.20e4 42401
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_co~ 2.49e5 42401
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_co~ 3.65e5 42401
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_co~ 4.55e5 42401
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_co~ 7.25e5 42401
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_co~ 9.29e5 42401
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_co~ 9.05e5 42401
## 8 adult confirmed 80+ previous_day_admission_adult_covid_co~ 7.73e5 42401
## 9 adult suspected 0-19 previous_day_admission_adult_covid_su~ 3.35e4 42401
## 10 adult suspected 20-29 previous_day_admission_adult_covid_su~ 2.27e5 42401
## 11 adult suspected 30-39 previous_day_admission_adult_covid_su~ 2.97e5 42401
## 12 adult suspected 40-49 previous_day_admission_adult_covid_su~ 3.03e5 42401
## 13 adult suspected 50-59 previous_day_admission_adult_covid_su~ 4.79e5 42401
## 14 adult suspected 60-69 previous_day_admission_adult_covid_su~ 6.54e5 42401
## 15 adult suspected 70-79 previous_day_admission_adult_covid_su~ 6.32e5 42401
## 16 adult suspected 80+ previous_day_admission_adult_covid_su~ 5.75e5 42401
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covi~ 1.30e5 42401
## 18 ped suspected 0-19 all_pediatric_inpatient_bed_occupied 2.00e7 42401
## 19 ped suspected 0-19 previous_day_admission_pediatric_covi~ 3.19e5 42401# Create pivoted burden data
createBurdenPivot <- function(lst,
dataThru,
minDatePlot="2020-08-01",
plotByState=c(state.abb, "DC")
) {
# FUNCTION ARGUMENTS:
# lst: a processed list that includes sub-component $dfRaw$cdcHosp
# dataThru: character string to be used for 'data through'; most commonly MMM-YY
# minDatePlot: starting date for plots
# plotByState: states to be facetted for plot of hospitaliztions by age (FALSE means do not create plot)
# Convert minDatePlot to Date if passed as character
if ("character" %in% class(minDatePlot)) minDatePlot <- as.Date(minDatePlot)
# Create the hospitalized by age data
hospAge <- lst[["dfRaw"]][["cdcHosp"]] %>%
select(state,
date,
grep(x=names(.), pattern="previous_.*ed_\\d.*[9+]$", value=TRUE),
grep(x=names(.), pattern="previous_.*pediatric.*ed$", value=TRUE)
) %>%
pivot_longer(-c(state, date)) %>%
mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"),
adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"),
age=ifelse(adultPed=="ped",
"0-17",
stringr::str_replace_all(string=name, pattern=".*_", replacement="")
),
age=ifelse(age %in% c("0-17", "18-19"), "0-19", age),
div=as.character(state.division)[match(state, state.abb)]
)
# Create the pivoted burden data
dfPivot <- makeCaseHospDeath(dfHosp=hospAge, dfCaseDeath=lst[["dfPerCapita"]])
# Plot for overall trends by age group
p1 <- hospAge %>%
filter(state %in% c(state.abb, "DC"), !is.na(value)) %>%
mutate(ageBucket=age) %>%
group_by(date, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
arrange(date) %>%
group_by(ageBucket) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= minDatePlot) %>%
ggplot(aes(x=date, y=value7)) +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")"),
subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
) +
lims(y=c(0, NA))
# Create three main plots of hospitalized by age data
print(p1 + geom_line(aes(group=ageBucket, color=ageBucket), size=1) + scale_color_discrete("Age\nbucket"))
print(p1 + geom_col(aes(fill=ageBucket), position="stack") + scale_color_discrete("Age\nbucket"))
print(p1 + geom_col(aes(fill=ageBucket), position="fill") + scale_color_discrete("Age\nbucket"))
# Plot for trends by state and age group
if (!isFALSE(plotByState)) {
p2 <- hospAge %>%
filter(state %in% plotByState, !is.na(value)) %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= minDatePlot) %>%
ggplot(aes(x=date, y=value7)) +
geom_line(aes(color=ageBucket, group=ageBucket)) +
scale_color_discrete("Age\nbucket") +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")")
) +
lims(y=c(0, NA)) +
facet_wrap(~state, scales="free_y")
print(p2)
}
# Return key data (do not return plot objects)
list(hospAge=hospAge, dfPivot=dfPivot)
}
# Create pivoted burden data
burdenPivotList_220501 <- postProcessCDCDaily(cdc_daily_220501,
dataThruLabel="Apr 2022",
keyDatesBurden=c("2022-04-29", "2021-10-31",
"2021-04-30", "2020-10-31"
),
keyDatesVaccine=c("2022-04-29", "2021-12-31",
"2021-08-31", "2021-04-30"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospPerCap_220501 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"),
lst=burdenPivotList_220501,
popVar="pop2019",
excludeState=c(),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
burdenPivotList_220501$hospAge %>%
group_by(adultPed, confSusp, age, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n())## `summarise()` has grouped output by 'adultPed', 'confSusp', 'age'. You can override using the `.groups` argument.## # A tibble: 18 x 6
## # Groups: adultPed, confSusp, age [18]
## adultPed confSusp age name value n
## <chr> <chr> <chr> <chr> <dbl> <int>
## 1 adult confirmed 0-19 previous_day_admission_adult_covid_con~ 41964 42401
## 2 adult confirmed 20-29 previous_day_admission_adult_covid_con~ 249178 42401
## 3 adult confirmed 30-39 previous_day_admission_adult_covid_con~ 365345 42401
## 4 adult confirmed 40-49 previous_day_admission_adult_covid_con~ 454857 42401
## 5 adult confirmed 50-59 previous_day_admission_adult_covid_con~ 724912 42401
## 6 adult confirmed 60-69 previous_day_admission_adult_covid_con~ 929208 42401
## 7 adult confirmed 70-79 previous_day_admission_adult_covid_con~ 905339 42401
## 8 adult confirmed 80+ previous_day_admission_adult_covid_con~ 772981 42401
## 9 adult suspected 0-19 previous_day_admission_adult_covid_sus~ 33526 42401
## 10 adult suspected 20-29 previous_day_admission_adult_covid_sus~ 226590 42401
## 11 adult suspected 30-39 previous_day_admission_adult_covid_sus~ 296759 42401
## 12 adult suspected 40-49 previous_day_admission_adult_covid_sus~ 303443 42401
## 13 adult suspected 50-59 previous_day_admission_adult_covid_sus~ 479067 42401
## 14 adult suspected 60-69 previous_day_admission_adult_covid_sus~ 653801 42401
## 15 adult suspected 70-79 previous_day_admission_adult_covid_sus~ 632370 42401
## 16 adult suspected 80+ previous_day_admission_adult_covid_sus~ 574885 42401
## 17 ped confirmed 0-19 previous_day_admission_pediatric_covid~ 130296 42401
## 18 ped suspected 0-19 previous_day_admission_pediatric_covid~ 318942 42401Peaks and valleys of key metrics are also updated:
peakValleyCDCDaily(cdc_daily_220501)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 6,192 x 8
## date state vxa vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # ... with 6,182 more rowsHospital capacity maps with imputed capacity are created:
modStateHosp_20220501 <- skinnyHHS(indivHosp_20220501) %>%
imputeNACapacity() %>%
sumImputedHHS()
# ICU summary
createGeoMap(modStateHosp_20220501,
yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"),
"pctICU"=c("label"="Total", "color"="black")
),
fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds),
"pctCovidICU"=expression(adult_icu_covid/icu_beds)
),
plotTitle="Average % ICU Capacity Filled by Week",
plotSubtitle="August 2020 to April 2022",
plotScaleLabel="% ICU\nUsed",
returnData=FALSE
)
# Adult beds summary
# createGeoMap(modStateHosp_20220416 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
createGeoMap(modStateHosp_20220501 %>% filter(!(state %in% c("CT", "DE", "SD", "AK"))),
yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"),
"pctAdult"=c("label"="Total", "color"="black")
),
fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds),
"pctCovidAdult"=expression(adult_beds_covid/adult_beds)
),
plotTitle="Average % Adult Beds Capacity Filled by Week",
plotSubtitle="August 2020 to April 2022\n(AK, CT, DE, and SD data excluded)",
plotScaleLabel="% Adult\nBeds\nUsed",
returnData=FALSE
)
A function is created for hospital post-processing:
hospitalCapacityCDCDaily <- function(df,
createData=TRUE,
returnData=createData,
maxCapacity=1.2,
plotSub="start to finish"
) {
# FUNCTION ARGUMENTS:
# df: the key data frame
# createData: boolean, if TRUE then convert df for use in processing
# if FALSE, use df as-is
# returnData: boolean, should df be returned (defaults to TRUE is modified, FALSE otherwise)
# maxCapacity: states that exceed this capacity level will not be plotted (explore separately)
# plotSub: subtitle to use for plots
# Convert data if requested
if(isTRUE(createData)) df <- skinnyHHS(df) %>% imputeNACapacity() %>% sumImputedHHS()
# Create ICU summary
createGeoMap(df,
yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"),
"pctICU"=c("label"="Total", "color"="black")
),
fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds),
"pctCovidICU"=expression(adult_icu_covid/icu_beds)
),
plotTitle="Average % ICU Capacity Filled by Week",
plotSubtitle=plotSub,
plotScaleLabel="% ICU\nUsed",
returnData=FALSE
)
# Get list of states that may complicate map
pctState <- df %>%
mutate(pctAdult=adult_beds_occupied/adult_beds, pctCovidAdult=adult_beds_covid/adult_beds)
exclStates <- pctState %>% filter(pctAdult > maxCapacity) %>% count(state) %>% pull(state)
if(length(exclStates) > 0) plotSub <- paste0(plotSub, "\n(", paste(exclStates, collapse=", "), " data excluded)")
# Create the adult beds summary
createGeoMap(df %>% filter(!(state %in% all_of(exclStates))),
yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"),
"pctAdult"=c("label"="Total", "color"="black")
),
fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds),
"pctCovidAdult"=expression(adult_beds_covid/adult_beds)
),
plotTitle="Average % Adult Beds Capacity Filled by Week",
plotSubtitle=plotSub,
plotScaleLabel="% Adult\nBeds\nUsed",
returnData=FALSE
)
# Return the data if requested (defaults to only if createData is TRUE)
if(isTRUE(returnData)) return(df)
}
hospitalCapacityCDCDaily(modStateHosp_20220501, createData=FALSE, plotSub="August 2020 to April 2022")